Data Management Complete Study Guide 3g11y

CDMP Certified Data Management Professional

Data Management Examination Guide Data Exam Series Vol. 1

The Education & Research of DAMA International

The Data Management Examination Guide

Acknowledgement is made for permission to use the full exam outline from the tly developed DAMA International – Institute for Certification of Computing Professionals (IC) Data Management exam outline, copyright © 2006 IC. Written by Diane C. Johnson, PMP, for DAMA International & DAMA International Foundation. Published by DAMA International & DAMA International Foundation, Bellevue, WA, U.S.A. Data Management Examination Guide Data Exam Series, Vol. 1 To order copies, please

[email protected] PO Box 5786 Bellevue, WA 98006-5786 1-425-562-2636 www.dama.org

For exam istration questions, please

[email protected] or call 1.800.843.8227 2350 E. Devon Avenue, Suite 115, Des Plaines, IL 60018 USA www.ic.org

Copyright © 2006 by DAMA International & DAMA International Foundation All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form, or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior consent of the publisher. ISBN 0-9676674-3-7 First Edition, April 2006

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The Data Management Examination Guide

Table of Contents Introduction......................................................................................................................... 4 How Do You Obtain a CDMP? ...................................................................................... 5 CDMP Examination Criteria........................................................................................... 5 Additional CDMP Certification Criteria......................................................................... 6 Recommended Exams Based on Candidate’s Work Experience.................................... 8 Preparation for Taking Exams ........................................................................................ 9 Taking CDMP Exams ..................................................................................................... 9 Professional Development / Recertification ................................................................. 10 CDMP Information.......................................................................................... 10 IC Data Management Specialty Examination Outline................................................. 12 How To Read The Data Management Subject Outline ................................................ 12 Data Management Exam Subject Outline..................................................................... 13 1.0 Data Management Function........................................................................................ 21 Overview....................................................................................................................... 21 Topics............................................................................................................................ 21 Questions....................................................................................................................... 22 Quick Answers.............................................................................................................. 33 Detailed Answers .......................................................................................................... 34 2.0 Data & Metadata Infrastructures Creation / Maintenance .......................................... 42 Overview....................................................................................................................... 42 Topics............................................................................................................................ 42 Questions....................................................................................................................... 43 Quick Answers.............................................................................................................. 51 Detailed Answers .......................................................................................................... 52 3.0 Data Analysis and Modeling....................................................................................... 57 Overview....................................................................................................................... 57 Topics............................................................................................................................ 57 Questions....................................................................................................................... 58 Quick Answers.............................................................................................................. 76 Detailed Answers .......................................................................................................... 77 4.0 Data / Metadata Infrastructure Management .............................................................. 88 Overview....................................................................................................................... 88 Topics............................................................................................................................ 88 Questions....................................................................................................................... 89 Quick Answers.............................................................................................................. 96 Detailed Answers .......................................................................................................... 97 5.0 Information Quality Management............................................................................. 102 Overview..................................................................................................................... 102 Topics.......................................................................................................................... 102 Questions..................................................................................................................... 103 Quick Answers............................................................................................................ 114 Detailed Answers ........................................................................................................ 115 Selected Bibliography..................................................................................................... 122

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The Data Management Examination Guide

Introduction The Certified Data Management Professional (CDMP) credential validates knowledge and experience of Data Management Professionals. CDMP Credentials can be a doorway to opportunities either measuring your standing by demonstrating Mastery level or providing a starting point for new professions through a Practitioner designation. The Certified Data Management Professional (CDMP) credential is awarded to those who qualify based on a combination of criteria including education, experience and testbased examination of professional level knowledge. To maintain certified status and continued use of the credential, an annual recertification fee along with a 3-year cycle of continuing education and professional activity is required. The Data Management Association International (DAMA) authorizes the Certified Data Management Professional certification program and granting of the CDMP designation in partnership with the Institute for Certification of Computing Professionals (IC), which isters testing and recertification. The IC Data Management exam is meant to be an experience exam, meaning that it tests what you know at the time. This study guide is meant to be a refresher to test taking and the concepts behind data management. You can focus on the sections that you need to learn, or take the practise exam to see where your strengths lie. The study guide is broken down into the five major sections of the exam: 1.0 Data Management Function 2.0 Data & Metadata Infrastructures Creation / Maintenance 3.0 Data Analysis and Modeling 4.0.Data / Metadata Infrastructure Management 5.0 Information Quality Management The DAMA International Foundation welcomes on this Study Guide, as revisions will occur in the future. We encourage you to let us know how you are using these materials and how they might be improved. Your comments can be sent to: Vice President of Education, [email protected]

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The Data Management Examination Guide

Overview of the Certified Data Management Professional (CDMP) Certification Program How Do You Obtain a CDMP? The CDMP certification process is as follows: 1. 2. 3. 4. 5.

Fill out an application (from www.dama.org or www.ic.org). Arrange to take the exam(s). the IT Core exam (required). Take two specialty exams. One specialty exam must be taken from a. Data Management b. Data Warehousing c. Database istration 6. Meet the experience and education qualifications. 7. Sign the IC code of ethics. There is a professional development / recertification aspect to keeping your certification current after you are certified. This recertification activity is handled through the IC office.

CDMP Examination Criteria Three IC exams must be ed with the following scores: Score all exams at 50% or higher all exams at 70% or higher

Credential Earned CDMP Practitioner Certificate CDMP Mastery Certificate

The CDMP Practitioner certification is awarded to professionals who scored above 50% on all three exams. These individuals can contribute as a team member on assigned tasks for they have a working knowledge of concepts, skills and techniques in a particular data specialization. The CDMP Mastery certification is awarded to professionals who scored 70% or higher on all three exams. These individuals have the ability to lead and mentor a team of professionals as they have mastered the concepts, skills and practices of their data specialization. Exams may be retaken to improve your score and go from the Practitioner to the Mastery certificate level. You may be able to substitute select vendor certifications for up to one specialty exam.

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The Data Management Examination Guide

Additional CDMP Certification Criteria The following criteria must also be met in order to qualify for the CDMP: CDMP Criteria # Years Data Professional Work Experience Substitute Up to 2 Years –Bachelor or Master Degree in an appropriate discipline for Work Experience Recertification Required Continuing Professional Education/Activity Required IC Code of Ethics

CDMP Practitioner Certificate 2

CDMP Mastery Certificate 4+

2

2

Yes

Yes

120 hours every 3-year cycle

120 hours every 3-year cycle

Yes

Yes

Sample Qualifications for the CDMP Other qualifications may be accepted. Check with the DAMA s or IC office. Education Bachelor of Science Degree Major in: Computer Science Information Systems Management Information Systems Information and Communications Technology Major in another discipline with minor in any of the above Masters Degree Computer Science Information Systems Information Resource Management Information and Communications Technology MBA with concentration in one of the above

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The Data Management Examination Guide

Work Experience Sample qualifying position/role titles: VP, Director, or Manager of Data Management Data Architect, Data , Data Analyst, Data Modeler Data Specialist, Database , Data Warehousing Analyst Systems Architect, Systems Analyst, Project Manager, Project Leader Business Analyst, Repository Analyst, Repository Architect

Professional Examinations The CDMP requires three IC exams: IT Core, one specified data oriented exam, and one other exam. If you already ed one or more IC exams, these exams can be used toward a CDMP if considered current by IC standards, and the exams are listed within your CDMP area of specialization. For information on your status, the IC. If you wish to demonstrate expertise in exam specialty areas specifically, the IC will issue Expert (Proficiency) Certificates for each specialty exam ed at 70% or higher. If you wish to know how these exams were developed, go to (http://www.ic.org/icnew/iwg2.html). These exams are product and vendor neutral, and international in scope.

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The Data Management Examination Guide

Suggested Exams Based on Candidate’s Work Experience The following table shows the Data Management areas by which IC exams are either suggested (S) or your choice (C) for a total of three exams. The IT Core exam is required for all candidates. Your work experience in the field will let you determine what exams you are best suited to . IC Exams

Mgmt

Architecture

Data Analysis & Design

DBA

Data Warehousing

Metadata / Repository Mgmt

Data / Information Quality (Future)

Req’d

Req’d

Req’d

Req’d

Req’d

Req’d

Req’d

S

S

S

C

C

S

S

Database istration

C

C

S

C

C

C

Data Warehousing

C

C

C

S

C

C C

IT Core Specialty Exams Data Management

Integrated Project Mgmt

C

C

C

IT Management

C

C

C

Systems Development

C

Object Oriented Analysis & Design

C

C

C

C

Systems Security

C

Future IC Exams Business Intelligence & Analytics

S

Data & Information Quality Acceptable Exam Substitutes (Third Party)

C

C

C

C

C

C

S

C

C

C

C

C (future: e.g. MIT or Berkeley – DQ programs)

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The Data Management Examination Guide

Accepted Vendor/Training Certifications Any of the following certifications may be substituted for one of the "candidate's choice" specialty exams required for the CDMP. Other certification programs may be accepted, but need to be evaluated. Check with the IC office or the DAMA s. IBM - IBM Certified Database - DB2 Universal Database - IBM Certified Advanced Database – DB2 Universal Database - IBM Certified Solutions Expert - DB2 Universal Database - IBM Certified Solutions Expert - DB2 Content Manager Information Engineering Services Pty Ltd - Certified Business Data Modeller Insurance Data Management Association (IDMA) - Certified Insurance Data Manager Microsoft - Microsoft Certified Database NCR (Teradata) - Teradata Certified Professional Oracle - Oracle (xx) Certified Professional - Oracle9i Database Certified Professional (for Practitioner Level CDMP) - Oracle9i Database Certified Master (for Mastery Level CDMP) Project Management Institute - Project Management Professional (PMP)

Preparation for Taking Exams There are various ways of learning the process of taking IC exams: • Sponsor IC Exam Review courses for your DAMA chapter hip • Refer to the exam subject outlines (at level 1 & 2) posted on http://www.ic.org/icnew/outlines.html to become familiar with the subject coverage of each exam • the IC for the CDMP Study Guide which covers all the exams in the CDMP program and has sample exams/questions for self-study • DAMA International for the Data Management Exam Study Guide. Other individual data exam study guides are planned for the future. The IC exams are also offered at the DAMA International Symposiums.

Taking CDMP Exams IC Testing can be done anywhere in the world, with an approved IC Proctor to physical identity and supervise/invigilate the delivery of the examination.

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The Data Management Examination Guide

A DAMA chapter can set up exam sessions during their chapter meetings. What is needed is a volunteer proctor from the chapter. A proctor is an individual authorized by IC to oversee the writing of an exam by an IC exam taker. This person must meet specific guidelines (http://www.ic.org/icnew/testing.html) and be willing to supervise the exam taker. The IC reserves the right to reject proposed proctors. [email protected] or phone 847.299.4227 or 800.843.8227 if you require assistance in determining an appropriate proctor. The exams run off the USB drive of an individual’s laptop. There are 110 questions with 110 being scored and 10 are beta questions to complete in 90 minutes. You will not know which type of question you are answering. Questions and possible distracters (answers) are randomly listed in a different order for each exam taker. Therefore, although this guide contains sample questions that allow for “all or none of the above” type answers meant for study purposes, you will not find this type of answer to choose from on the actual exam. Computer based testing allows for the immediate scoring after the exam is taken. An IC Performance Profile is then available for ing, and one will be sent later to the individual by the IC. This Profile shows your exam strengths and weaknesses.

Professional Development / Recertification To keep your CDMP current, you must earn 120 approved hours of continuing education over a 3-year period. Many educational activities count including DAMA Symposiums and chapter meetings. For further information, the IC ([email protected]) for an IC Recertification Guidelines Booklet or go to www.ic.org/icnew/Recertification%20Guidelines2005.pdf. Recertification credits can be entered on an IC Educational Activity Form or through www.ic.org/cgi-win/pdform.exe. Your DAMA chapter can also keep track of meeting attendance for the purpose of recertification and submit on a timely basis. There is an annual maintenance fee to IC for keeping track of your recertification credits. You will receive an annual transcript from the IC.

CDMP Information For Questions on the CDMP Certification Program: DAMA International IC Directors at: [email protected] P.O. Box 5786 Bellevue, WA 98006-5786 415-562-2636 Page 10 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

The Data Management Examination Guide

The DAMA website is www.dama.org for further information and an application. To Order the IC CDMP Study Guide or For Questions on CDMP Testing, istration and Recertification: the IC Office at: 847-299-4227 or 800-843-8227 (phone) 847-299-4280 (fax), or [email protected]. The IC website is www.ic.org for further information and an application.

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The Data Management Examination Guide

IC Data Management Specialty Examination Outline How To Read The Data Management Subject Outline In the examination outline, a consistent set of syntax conventions has been used: • • •

•

Outline elements with numeric level leaders imply inclusivity. Concepts not within the numbered structure will not be tested. Outline elements with a “•” bullet leader are examples to clarify the content of a numbered element, and are not necessarily inclusive. Numbers in parentheses after an element name indicate the number of questions in the exam, which will be presented on the subject indicated by the element name and all subordinate elements. These allocations are guidelines established by the Test Management Council, and are followed as closely as possible in selecting questions for the exam. There are 100 multiple-choice questions on each exam version and this outline reflects this total. The characters “D#” after an element name indicate the target “depth” of questions to be posed on the subject indicated by the element name and all subordinate elements. The depths of knowledge are defined as follows: D1 D2 D3 D4 D5 D6

Recognition Knowing what a concept is called. Differentiation Knowing the external differences between a concept and a neighboring concept. Description Knowing the external characteristics of a concept. Usage Knowing how to use instances of the concept and why. Structure Knowing the internal structure of the concept — its components and the relationships among these components. Construction Knowing how to put together instances of the concept tailored to specific purposes.

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The Data Management Examination Guide

Data Management Exam Subject Outline 1.0. Data Management Function

(18) Section 1 Total

1.1 Planning

(8)

1.1.1. • • • 1.1.2. • • • •

D4

Scope & Charter Data Management customer base Vision, goals, objectives Functions / services Data Management Plans Strategic data management plan (linked to business plan) Organizational structure plan and budgets Metadata management budgets, metrics, audits Data management oversight/control, e.g., data standards approval committee, technology change management committee, data management process change management committee, enterprise data management ‘board of directors’ • Enterprise data / information framework • Data portfolio management plan • Relationship management plan (vendor, customer, employee) • Data quality management plan • Data management process maturity improvement plan • Data and data management configuration management plan • Data and data management standards management plan 1.1.3. Policies / Standards / Processes / Procedures / Guidelines • Internal to data management organization • Customer data / metadata guidelines 1.2. Organization 1.2.1. • • 1.2.2. • • •

(2)

D3

(8)

D3

Types of Staff Training Orientation for new employees Continuing education for required skills, retraining Communication Marketing data services and benefits Customer education / training Publishing newsletters and web site news

1.3. Roles & Responsibilities 1.3.1. Data istration • Data planning, policy development • Data architecture

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The Data Management Examination Guide

• • • • • 1.3.2. • • • • 1.3.3. • • • • • 1.3.4. • • • 1.3.5. • • • • • • • • 1.3.6 • • • •

Data requirements modeling: conceptual (entity types and their relationships), logical (ERA), and physical (database design) Data model management Data resource control and quality Standards management, setting, communication and enforcement Liaison with Database s, Business Analysts, Management, s Metadata istration Metadata planning, policy development Metadata requirements gathering Metamodeling (metadata modeling) Metadata tool istration (metadata registries and repositories) Database istration Definition and organization of physical database Protection and recovery of physical database Data archiving and deletion Optimization and documentation of physical databases Liaison with Data s, Business Analysts, Management, s Data Warehouse istration Warehouse modeling, design, implementation, and operation Operational data store modeling, design, implementation, and operation Data access istration Information Stewardship Business information steward Managerial information steward Physical data trustee Originator of business rules Information producer Data quality ability Metadata creation Information usage and knowledge worker stewardship Configuration Management Database Data models Data standards Metadata management tools

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The Data Management Examination Guide

2.0 Data & Metadata Infrastructures Creation / Maintenance (15) Section 2 Total 2.1 Planning for Data & Metadata 2.1.1 • • • • • • • • 2.1.2. • • • • •

D4

Architectures Enterprise Data Data Sourcing Data Distribution Data Integration Change Authorization Zachman Framework Data Processing Architectures (i.e. client-server, distributed data, etc.) Metadata Architectures Data Architecture Methods Information Engineering Enterprise Architecture Planning Data Life Cycle Data Reengineering Prototyping

2.2.Tools and Technology Types 2.2.1. • • • 2.2.2. • • • • • 2.2.3. • • •

(6)

(9)

Data Database Management Systems (DBMS & ODBMS) Data modeling tools Extract, transform, and load (ETL) tools Metadata & Descriptive Information Data dictionaries Data directories Data encyclopaedias Metadata registries (e.g. ISO/IEC 11179) Metadata repositories Data Issues Business intelligence technologies (OLAP, Data Mining, etc.) Data management and the Internet / Intranet Data management and unstructured data

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D3

The Data Management Examination Guide

3.0. Data Analysis and Modeling 3.1. Data / Metadata Analysis & Design 3.1.1. • • • • 3.1.2. • • • • • •

(6)

D5

Fact Finding Techniques Interviewing Surveys, questionnaires JAD sessions Legacy systems analysis Requirements Definition and Management Evaluation of current environment and documentation Future state Gap analysis Business rules (discovery, validation and documentation) Data / process matrices Requirements tracking and management to implementation

3.2. Data Model Components 3.2.1. • • • • • • • • • • 3.2.2. • • 3.2.3. • • • • 3.2.4. • • • •

(37) Section 3 Total

(21)

Logical Data Model Entity type Relationship type Attributes and their roles Definitions Key Cardinality Optionality Metadata type Rules: Business / data integrity Normalization Dimensional Warehouse Fact Dimension Object Oriented / UML Object Class type Attribute type Relationship type Data Representations in Process Models Business views / presentation level Trigger Stored procedure Object method

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D5

The Data Management Examination Guide

3.3. Data / Metadata Model Management 3.3.1. • • • • • • • • 3.3.2. • • • • 3.3.3. • • • • • • •

(10)

D5

Types of Data Models Conceptual Logical Physical Data warehouse Metamodels / meta-metamodels Universal / industry models Object class Data life cycle Scope of Model and Metadata Enterprise wide Business area Project oriented Subject area Data Model Creation Maintenance Version control Comparison Merging Importing / exporting Linkages and mappings between enterprise, logical, physical data models, and process models

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The Data Management Examination Guide

4.0.Data / Metadata Infrastructure Management 4.1. Standards, Policies, Procedures, Guidelines

(12) Section 4 Total (7)

D5

4.1.1. Standards Management Process • Awareness of external standards, e.g. ANSI and ISO/IEC data and data management related standards • Creation/identification • Approval • Enforcement • Maintenance 4.1.2. Data Models • Naming conventions for entities, relationships, attributes, etc. • Business and data integrity rules 4.1.3. Data Elements • Element types • Naming conventions • Metadata / definition principles • Legacy element linkages • Data element audit 4.2. Data Security and Privacy

(5)

4.2.1 Data Security Principles • ability • Authorization • Availability 4.2.2. Data Security Policy Types • Data stewardship and trustee responsibilities • Data and instance value access sensitivities (e.g. privacy, corporate confidentiality, data aggregation sensitivity issues) • Trans-border data flow • Data content

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D3

The Data Management Examination Guide

5.0. Information Quality Management 5.1. Information Quality Principles

(18) Section 5 Total (6)

D3

5.1.1. Definition • Data and Information • Information quality • Data definition as Information Product Specification • Data definition quality • Information architecture (data model) quality • Business drivers • Costs of nonquality information 5.1.2. Information Quality Characteristics • Conformance to definition • Completeness • Validity • Accuracy • Precision • Non duplication • Consistency of redundant data • Timeliness • Usefulness • Objectivity (of presentation) • Presentation clarity 5.1.3 Data Definition (or Information Product Specification) Quality Characteristics • Properly formed name, in accordance with approved naming convention standard • Standard, single enterprise abbreviations for new development • Name appropriate to knowledge workers • Correct, clear, and complete definition • Business term (used in data definition) defined in glossary • Correctly specified value domain and definition (of code values) • Properly defined data value type (not just alphanumeric, etc., but domain type (corresponding to class words, e.g., data, code, amount, ID, etc.) • Correct, complete, and useful business rule specification 5.2. Information Quality Assessment / Audit

(4)

5.2.1. Quality Assessment Characteristics • Data definition quality assessment process/techniques • Data model / requirements quality assessment process/techniques

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D4

The Data Management Examination Guide

5.2.2. Quality /Cost Measurement • Baseline data cost calculation (Measurement of cost of redundancy and interfaces) • Cost of non quality information • Value chain relationship between quality information and business drivers 5.3. Information Quality Improvement

(8)

5.3.1. Data Corrective Maintenance • Data correction of defective data • Redesign processes/systems producing poor quality data content or presentation 5.3.2. Data Movement Control • Mapping, transforming, data for data movement planning • Quality audit and control of data movement 5.3.3. Information Quality Process Improvement • Root Cause Analysis and Cause-and-Effect Diagrams • Shewhart Cycle (Plan-Do-Check-Act) process for improvement • Information defect prevention techniques 5.3.4. Information Quality Culture Transformation • Employee training in information quality techniques • Management ability for information quality (managerial information stewardship • Information quality management maturity assessment • Gap analysis • Information quality performance measures

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D3

The Data Management Examination Guide

1.0 Data Management Function Overview The Data Management mission consists of goals and objectives that guide the creation, use, management and deletion of data across enterprise. Data Managements needs to be planned into overall Information Technology Strategy which is linked to the Business plan. To the functions and services related to Data Management within the organization, a dedicated group of professionals with oversight committees need to be in place e.g., data standards approval committee, technology change management committee. An important aspect of data management oversight is to establish common Policies, Standards Procedures and Guidelines for data ontology. services of training and communication will further the knowledge and usage of Data Management within an organization. When new employees are hired, orientation sessions are required from the Data Management group to gain an understanding of the data environment and the use of data to do their job. For individuals involved in the dayto-day management of the data, the training should be comprehensive and potentially involve mentoring of the Data Policies, Standards, Procedures and Guidelines. Communication of data services and updates through the use of newsletters assists in keeping the organization up-to-date with the progress and accomplishments of the group. To the Data Management Function, major roles and responsibilities for managing data need to be defined. There are no standards that define titles or team structure. Typically, there is a Manager role for the group that is responsible for planning, organizing and directing, plus may be a subject matter specialist. The job descriptions outlined in this section are based on standard guidelines of job titles and responsibilities based on experience.

Topics Data Management Planning Data Management Scope & Charter Data Management Plans Policies / Standards / Processes / Procedures / Guidelines Data Management Organization Types of Staff Training Communication Roles & Responsibilities Data istration Metadata istration Database istration Data Warehouse istration Information Stewardship Configuration Management

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The Data Management Examination Guide

Questions 1.1. Planning 1.1.1

Scope & Charter

1. What is the primary mission/vision of Data Management? A. To facilitate the development, management, and use of the data resources as a vital asset. B. Committed to excellence. C. Database maintenance and enhancement for production application systems D. Data analysis and modeling for projects in planning or analysis 2. Which is not an objective of Data Management? A. Maintain the physical integrity and efficiency of data resources. B. Education about the benefits of and methods for enhancing data quality. C. Provide the architecture and guidelines for documenting and implementing data resources. D. Provide a cost effective and robust document and content management capabilities, workflow and business process management capabilities. 3. Which of the following is not a valid scope of Data Management Function? A. Requirements analysis and modeling B. Enterprise-wide data coordination, integration, stewardship and use C. Data security and quality D. Economies of scale in purchasing. 4. Which one of the following is not a typical type of service in a Data Management function? A. for warehouse initiatives. B. Database maintenance and enhancement for production application systems C. Database design for projects in development D. Data analysis and modeling for projects in planning or analysis

1.1.2 Data Management Plans 5. The goal of the Data Management Plan is to describe the resources and process used to ensure high quality data. The Data Management Plan is usually part of what overall strategy? A. Information Technology Strategy B. Information Technology Infrastructure Strategy C. Application Infrastructure Plan D. Information Technology Architecture Plan Page 22 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

The Data Management Examination Guide

6. A Data Management Plan is usually part of what overall strategy? A. Information Technology Strategy B. Information Technology Infrastructure Strategy C. Application Infrastructure Plan D. Information Technology Architecture Plan 7. Which one of the following is not a part of a Data Management Plan? A. Describe the roles and resources of program staff. B. Define future direction of data management activities in a work plan. C. Implement facilities and tools for managing metadata resources. D. Development of a quality management plan. 8. Which Committee is not an oversight committee regarding Data Management? A. Data Standards Approval Committee B. Data Management Process Change Management Committee C. Enterprise Data Management Board of Directors D. Project Change Committee 9. What is the purpose of conducting a metadata management audit? A. To ensure metadata management controls have achieved intended results. B. Provide an additional source of information for the budget. C. To determine that decisions made in a timely fashion with appropriate criteria/guidance that uses all necessary data/information from automated systems and, if applicable, s. D. To determine that all appropriate policies/procedures been developed, disseminated, kept up to date, and test checks made to ensure compliance. 10. Benchmarking will provide comparative information. Which of the following is not a result of benchmarking? A. Efficiency and how well resources and services (outputs) are delivered B. Quality of services and extent to which customers are satisfied (outcomes) C. Measure for evaluating outcomes or the results of program activity compared to its intended purpose and program objectives D. Best practices 11. Which role is not typically involved in a data management oversight committee? A. Program Director/Manager B. s C. Database D. Data Analysts Page 23 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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12. Which one of the following is not a benefit of a strong data portfolio management program? A. Maximize value of IT data investments while minimizing the risk B. Improve communication and alignment between technology and business. C. Encourage reuse of data throughout the organization. D. Allow planners to schedule resources more efficiently. 13. Which one of the following is least desirable benefit of Enterprise data / information framework? A. Provides enterprise-wide definitions of concepts and data. B. Provides a scoping tool for new initiatives. C. Reduces data redundancy by providing transparency as to the meaning of data items D. Encourages re-use and consistent data structures across the enterprise 14. What party would not be considered when creating a relationship management plan? A. Vendor / Supplier B. Customer C. Employee D. President 15. A Relationship Management Plan when dealing with vendors / suppliers should be part of which overall strategy? A. Procurement Strategy B. Quality Management Strategy C. Enterprise Architecture Strategy D. IT Strategy 16. Which area does a Quality Management Plan does not address? A. Quality policies and procedures. B. Roles, responsibilities and authorities. C. Description of quality system. D. Meta-metamodel 17. Which one of the following is not true when describing Capability Maturity Model Integration (CMMI)? A. Model framework to assess data and process maturity. B. Model framework to determine priorities. C. Model framework to institute process and data improvement. D. Defines six levels of process maturity. Page 24 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

The Data Management Examination Guide

18. What level is organizations CMMI maturity, if the data management requirements are not being met? A. Level 0 B. Level 1 C. Level 2 D. Level 3 19. What level is organizations CMMI maturity, if the data management requirements are being met? A. Level 0 B. Level 1 C. Level 2 D. Level 3 20. What level is organizations CMMI maturity, if the data management requirements are being managed and tracked? A. Level 1 B. Level 2 C. Level 3 D. Level 4 21. What level is organizations CMMI maturity, if the data management requirements meet EIA Standard 859 Industry Standard for Data Management that includes nine high level Data Management Principles? A. Level 1 B. Level 2 C. Level 3 D. Level 4 22. Which one is not the purpose of the data management configuration management plan? A. Identify and describe the overall policies and methods for Configuration Management. B. Establish and provide the basis for a uniform and concise Configuration Management practice C. Manage the data for its entire lifecycle. D. Retain data commensurate with value.

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23. Who is responsible for developing and implementing Data-Management planning for projects, for ensuring that the activities are completed according to agreed standards and timelines and for coordinating ongoing data management to the business? A. Data Manager B. Data Analyst C. Database D. Business Manager

1.1.3 Policies / Standards / Processes / Procedures / Guidelines 24. What is the following statement: Data archives must include easily accessible information about the data holdings, including quality assessments, ing ancillary information, and guidance and aids for locating and obtaining the data? A. Policy B. Standard C. Procedure D. Guideline 25. What is the following statement: Information offers data groupings that are used to describe a point of , address, and communication information? A. Policy B. Standard C. Procedure D. Guideline 26. What is the following statement: To keep the hard drives from getting full, please back-up your data. 1. Put CD data you want to back up in one folder. The name of the folder is the name of the CD. 2. Start the "Backup" program. 3. Click on "Data." 4. Move the data you want to back up in "Data window." 5. Click on "Done." 6. Put the CD in the CD-R and close the door. 7. Write the CD. A. Policy B. Standard C. Procedure D. Guideline

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27. What is the following statement: Aggregation of data values is appropriate for fields with a large numbers of values, such as dates, age, and geographic areas; it is the primary method used to collapse a dataset in order to create tables with no small numbers as denominators or numerators in cells? A. Policy B. Standard C. Procedure D. Guideline 28. What is the following statement: Data custodians are responsible for creating and maintaining metadata for their datasets? A. Policy B. Standard C. Procedure D. Guideline 29. Which of the following is the best answer for the definition of cost when following the metadata procedure, to “state what the concept is, not only what it is not”. A. Total spent for goods or services including money and time and labor. B. Cost is a price paid. C. Costs, which are not related to external costs. D. Direct cost to the business owner of those items, which will be sold to customers.

1.2. Organization 1.2.1. Types of Staff Training 30. Which one of the following is not appropriate for an orientation of the data environment for new employees? A. Acronym list. B. Customer Service Policy. C. Data Policy and Procedure. D. WWW Design and Programming. 31. When embarking on continuing education for required skills or retraining, which training method is least desirable? A. Mentoring with another employee. B. Workshops and seminars. C. Classroom or computer based courses. D. Booklets and information sheets.

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1.2.2. Communication 32. Which one of the following is the least beneficial when promoting data services? A. Communicating data services and benefits. B. Publishing newsletters and web site news. C. Customer education and training. D. Convening a meeting of the Organizations Management Team. 1.3. 1.3.1.

Roles & Responsibilities Data istration

33. Who is responsible for identifying and analyzing information needs for the enterprise or business area, and develops and maintains data architecture? A. Data B. Manager, Data istration C. Data istration Consultant D. Database 34. Which one is not a responsibility of the Data ? A. Identify and analyze customer information needs. B. Develop and maintain data architecture. C. Develop and maintain strategic data plan. D. Provide approval authority over metadata policies and design. 35. When hiring a Data which skill is the least preferred? A. Relational Database experience. B. Logical and Physical Data Modeling. C. Project Management experience. D. Strong written and oral communication skills. 36. Which role would a Data not typically interact? A. Business Analyst B. CEO C. Repository D. Management

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37. Who is responsible for planning, organizing, directing and controlling data definition, data use, and ensure data availability for the enterprise? A. Data B. Manager, Data istration C. Metadata D. Database

1.3.2. Metadata istration 38. Who is responsible for creating, istrating and enforcing of standards, guidelines and procedures for the use of metadata? A. Data B. Manager, Data istration C. Metadata D. Database 39. Which is not a responsibility of the Metadata role? A. Establish and maintain the metadata architecture. B. Provide approval authority over metadata policies and design. C. Maintain repository security profiles. D. Provide final review and approval authority over data design for an application system. 40. In a company with a Metadata team, which role would collect the requirements and design the metadata solution? A. Metadata . B. Manager, Metadata istration. C. Metadata Analyst. D. Metamodelers.

1.3.3. Database istration 41. Which responsibility is not typically a responsibility of the Database ? A. Establish and maintain sound backup and recovery policies and procedures. B. Implement and maintain database security (create and maintain s and roles, assign privileges). C. Perform database tuning and performance monitoring. D. Perform application tuning and performance monitoring.

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42. Which role would a Database not typically interact? A. Business Analyst B. CEO C. Data D. Management 43. When hiring a Database which skill is the least preferred? A. Relational Database, related utilities and tools experience. B. Physical Data Modeling. C. Ability to perform both Relational Database and Operating System performance tuning and monitoring. D. Network security istration. 44. Which role in an organization would develop the referential integrity constraint scripts? A. Data B. Manager, Data istration C. Data Analyst D. Database 45. Who has the responsibility to recover the physical database in the event of a power disruption? A. Data B. Manager, Data istration C. Data Analyst D. Database

1.3.4 Data Warehouse istration 46. Which responsibility is not typically a responsibility of the Data Warehouse ? A. Data Warehouse data modeling and design. B. Data Warehouse implementation and refresh. C. Data Access istration. D. Installing the Operating System on the Data Warehouse server.

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47. When hiring a Data Warehouse which skill is the least preferred? A. Relational Database, related utilities and tools experience. B. Ability to calculate Data Warehouse return on investment, costs and benefits. C. Expert in data structure including parallel data structure. D. Logical and Physical Data Modeling.

1.3.5. Information Stewardship 48. Which role would review and approve data definitions and domain value specifications for business data? A. Business Information Steward B. Managerial Information Steward C. Physical Data Trustee D. Information Producer 49. Who has authority to select and mandate Business Information Stewards? A. Business Information Steward B. Managerial Information Steward C. Physical Data Trustee D. Information Producer 50. Which role is not responsible for data quality? A. Business Information Steward or Managerial Information Steward B. Physical Data Trustee C. Information Producer D. Everyone is responsible for data quality. 51. Which one is not a responsibility of the Physical data trustee? A. Creation of data standards. B. Enforcement of physical security. C. Performance tuning of physical databases. D. Backup and Recovery of physical databases. 52. Who should be responsible for creating the data dictionary entries? A. Repository B. End C. Data Modeler D. Data Librarian

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1.3.6

Configuration Management

53. A new data model is created and rolled into Production. Which process is responsible for ing the modification in the Configuration Management Database (CMDB)? A. Change Management B. Configuration Management C. Problem Management D. Release Management 54. Which of the following is a Configuration Item (CI)? A. Organization Structure B. Data Model C. An incident D. A process 55. Which one is not a discipline of Data Management Configuration? A. Status ing B. Collection C. Approval D. Distribution 56. Which item is not a responsibility of the Configuration and Data Management team? A. Management of all documentation and specifications. B. Configuration and data management of programs. C. Maintaining requirements of deliverables through the data change process. D. Providing storage, retrieval, distribution, and management of program data.

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Quick Answers 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

A D D A A A C D A D C D B D A D D A B

20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38.

B C D A A B C D A A D D D A D C B B C

39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56.

D C D B D D D D B A B D A B B B A A

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Detailed Answers 1. Answer: A. To facilitate the development, management, and use of the data resources as a vital asset. The primary mission of Data Management is to facilitate the development, management, and use of the data resources as a vital asset. The services of Data Management comprise of the following: Database maintenance and enhancement for production application systems and Data analysis and modeling for projects in planning or analysis. 2. Answer: D. Provide a cost effective and robust document and content management capabilities, workflow and business process management capabilities. Objectives of Data Management are: Maintain the physical integrity and efficiency of data resources; education about the benefits of and methods for enhancing data quality; and provide the architecture and guidelines for documenting and implementing data resources. 3. Answer: D. Economies of scale in purchasing. The scope of Data Management function include: Requirements analysis and modeling; Enterprise-wide data coordination, integration, stewardship and use; and data security and quality. 4. Answer: A. for warehouse initiatives. Data Management services include data maintenance and enhancement for production application systems; Database design for projects in development; and Data analysis and modeling for projects in planning or analysis. 5. Answer: A. Information Technology Strategy. A Data Management Plan is usually part of the overall Information Technology Strategy. The Information Technology Strategy leads to Infrastructure Strategy, Information Technology Architecture Plan and Application Infrastructure Plan. 6. Answer: A. Information Technology Strategy. A Data Management Plan is usually part of the overall Information Technology Strategy. The Information Technology Strategy leads to Infrastructure Strategy, Information Technology Architecture Plan and Application Infrastructure Plan. 7. Answer: C. Implement facilities and tools for managing metadata resources. Data Management Plans are high level and describe the roles and resources of program staff, define future direction of data management activities in a work plan and the development of a quality management plan. 8. Answer: D. Project Change Committee. Data Management oversight committees have been called: Data Standards Approval Committee; Data Management Process Change Management Committee; and Enterprise Data Management Board of Directors. Project Change Committee refers to changes made to project scope, time, or cost. 9. Answer: A. To ensure metadata management controls have achieved intended results. The purpose of conducting a metadata management audit is to ensure metadata Page 34 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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management controls have achieved intended results as systematic and proactive measures. Conducting a Metadata Management Control Review assists managers in developing and implementing appropriate, cost-effective management controls for results-oriented metadata management; assessing the adequacy of metadata management controls in programs and operations; identify needed improvements; take corresponding corrective action; and report on the status of metadata management control improvements. 10. Answer: D. Best practices. The results of benchmarking will measure: efficiency and how well resources and services (outputs) are delivered; quality of services and extent to which customers are satisfied (outcomes); and measure for evaluating outcomes or the results of program activity compared to its intended purpose and program objectives. Only one benchmark will not provide comparative information. After measuring over time, will benchmarks will provide comparative information. 11. Answer: C. Database . Typically the data management oversight committee comprises of Program Director/Manager, s, and Data Analysts. A Database is not typically part of the data management oversight committee. 12. Answer: D. Allow planners to schedule resources more efficiently. A strong data portfolio management program 13. Answer: B. Provides a scoping tool for new initiatives. The least desirable benefit of Enterprise data / information framework is provides a scoping tool for new initiatives. New initiatives are first scoped by the business needs to gain the requirements. Enterprise data / information framework: Provides an enterprise-wide definitions of concepts and data; Reduces data redundancy by providing transparency as to the meaning of data items; and Encourages re-use and consistent data structures across the enterprise. 14. Answer: D. President. When creating a relationship management plan, the vendor, customer and employee should be considered. A relationship management plan should be approved for each project and the goal is to improve consistency in the way we approach relationships. 15. Answer: A. Procurement Strategy. A Relationship Management Plan when dealing with vendors / suppliers should be part of the Procurement Strategy. The Procurement Strategy should address when and how potential suppliers are to be engaged in the development of Relationship Management Plans. 16. Answer: D. Meta-metamodel. The Quality Management Plan addresses: quality policies and procedures; roles, responsibilities and authorities; and description of quality system. 17. Answer: D. Defines six levels of process maturity. The Capability Maturity Model defines five levels of process maturity; Model framework to assess data and process Page 35 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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maturity; Model framework to determine priorities; and Model framework to institute process and data improvement. 18. Answer: A. Level 0. If the data management requirements are not being met, the organization is at CMMI maturity level 0. CMMI Levels: 0 – not performed, 1 – initial, 2 – managed (disciplined process); 3 Defined (Standard, consistent process); 4 – Quantitatively Managed (Measured Predictable Process); 5 – Optimizing (Continuously Improving Process). 19. Answer: B. Level 1. If the data management requirements are being met, the organization is at CMMI maturity level 1. CMMI Levels: 0 – not performed, 1 – initial, 2 – managed (disciplined process); 3 Defined (Standard, consistent process); 4 – Quantitatively Managed (Measured Predictable Process); 5 – Optimizing (Continuously Improving Process). 20. Answer: B. Level 2. If the data management requirements are being met, the organization is at CMMI maturity level 2. CMMI Levels: 0 – not performed, 1 – initial, 2 – managed (disciplined process); 3 Defined (Standard, consistent process); 4 – Quantitatively Managed (Measured Predictable Process); 5 – Optimizing (Continuously Improving Process). 21. Answer: C. Level 3. If the data management requirements meet EIA standard 859 Industry Standard for Data Management, the organization is at CMMI maturity level 3. CMMI Levels: 0 – not performed, 1 – initial, 2 – managed (disciplined process); 3 Defined (Standard, consistent process); 4 – Quantitatively Managed (Measured Predictable Process); 5 – Optimizing (Continuously Improving Process). EIA standard 859 includes nine high level Data Management Principles. The principles address functions of Data Management: 1. Define the organizationally relevant scope of Data Management. 2. Plan for, acquire, and provide data responsive to customer requirements. 3. Develop DM processes to fit the context and business environment in which they will be performed. 4. Identify data products and views so their requirements and attributes can be controlled. 5. Control data repositories, data products, data views, and metadata using approved change control process. 6. Establish and maintain an identification process for intellectual property, proprietary, and competition-sensitive data. 7. Retain data commensurate with value. 8. Continuously improve data management. 9. Effectively integrate data management and knowledge management. 22. Answer: D. Retain data commensurate with value. The purpose of the data management configuration management plan is to identify and describe the overall policies and methods for Configuration Management; Establish and provide the basis for

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a uniform and concise Configuration Management practice; and Manage the data for its entire lifecycle. 23. Answer: A. Data Manager. A data manager is responsible for developing and implementing Data-Management planning for projects, for ensuring that the activities are completed according to agreed standards and timelines and for coordinating ongoing data management to the business, known as the data management standards management plan. 24. Answer: A. Policy. The sentence is a Policy statement: Data archives must include easily accessible information about the data holdings, including quality assessments, ing ancillary information, and guidance and aids for locating and obtaining the data. A Policy is a prescribed or proscribed course of action or behavior, which is to be followed with respect to the acquisition, deployment, implementation or use of information technology resources. It is not a standard, as it does not outline a specific technical approach. It is not a procedure, as it does not offer a set of istrative instructions for implementation of a policy or standard. It is not guideline that should offer a detailed plan or explanation to guide you in setting standards or determining a course of action. 25. Answer: B. Standard. The sentence is a Standard statement: Information offers data groupings that are used to describe a point of , address, and communication information. Standard(s) is a prescribed or proscribed specific technical approach, solution, methodology, product or protocol which must be adhered to in the design, development, implementation or upgrade of data architecture. Standards are intended to establish uniformity in data. Standards should be designated as either "preferred" or "mandatory". It is not a procedure, as it does not offer a set of istrative instructions for implementation of a policy or standard. It is not a guideline, which should offer a detailed plan or explanation to guide you in setting standards or determining a course of action. 26. Answer: C. Procedure. The sentence is a Procedure statement: To keep the hard drives from getting full, please back-up your data. Procedure is a set of istrative instructions for implementation of a policy or standard. It is not a guideline, which should offer a detailed plan or explanation to guide you in setting standards or determining a course of action. 27. Answer: D. Guideline. The sentence is a Guideline statement: Aggregation of data values is appropriate for fields with a large numbers of values, such as dates, age, and geographic areas; it is the primary method used to collapse a dataset in order to create tables with no small numbers as denominators or numerators in cells. A guideline offers a detailed plan or explanation to guide you in setting standards or determining a course of action. 28. Answer: A. Policy. The sentence is a Metadata Policy statement: Data custodians are responsible for creating and maintaining metadata for their datasets A Policy is a Page 37 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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prescribed or proscribed course of action or behavior, which is to be followed with respect to the acquisition, deployment, implementation or use of information technology resources. It is not a standard, as it does not outline a specific technical approach. It is not a procedure, as it does not offer a set of istrative instructions for implementation of a policy or standard. It is not guideline that should offer a detailed plan or explanation to guide you in setting standards or determining a course of action. 29. Answer: A. Total spent for goods or services including money and time and labor. When creating metadata definitions a guideline to put in place according to ISO/IEC 11179, is to “state what the concept is, not only what it is not”. 30. Answer: D. WWW Design and Programming. When creating an orientation of the data environment for new employees it is appropriate to have items like: Acronym list, Customer Service Policy, Data Policy and Procedure, Data Standards, Data Guidelines, IT Strategy, and potentially even Data Models where appropriate. WWW Design and Programming would be most beneficial to Analyst and Programmers. 31. Answer: D. Booklets and information sheets. When embarking on continuing education for required skills or retraining the least desirable method of training is booklets and information sheets due to the non-interactiveness of the information. 32. Answer: D. Convening a meeting of the Organizations Management Team. When promoting data services, the goal is to publicize the efforts to the Organizations data customers. Convening a meeting of the Organization Management Team only reaches a limited audience and they may not be suitable people. Using a wide variety of marketing and communication vehicles will assist in targeting the message like: Marketing data services and benefits; Customer education / training; and Publishing newsletters and web site news. 33. Answer: A. Data . The Data identifies and analyzes information needs for the enterprise or business area, and develops and maintains data architecture plus the strategic data plan. The Data provides project for data processing applications like data modeling and deg physical database. The Metadata is responsible for creating, istrating and enforcing of standards, guidelines and procedures for the use of metadata plus metadata query and analysis tools. The Manager, Data istration is responsible for planning, organizing, directing and controlling data definition, data use, and ensuring data availability for the enterprise. 34. Answer: D. Provide approval authority over metadata policies and design. The Data identifies and analyzes information needs for the enterprise or business area, and develops and maintains data architecture plus the strategic data plan. The Data provides project for data processing applications like data modeling and deg physical database. A Metadata would provide approval authority over repository policies and design. A Metadata would work with the Data . Additional responsibilities for the Data Page 38 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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include developing and enforcing standards for metadata through the review of definitions. Also, assist in developing procedures and data management policies that ensure the integrity, consistency and control of the enterprise's data resource. 35. Answer: C. Project Management experience. When hiring a Data the skill that may be desirable but least preferred in the above list is Project Management experience. Typically, skills for Data s are: Relational Database experience, logical and physical data modeling, strong written and oral communication skills, strong analysis skills and prior work experience. 36. Answer: B. CEO. The Data would not typically interact with the CEO in an organization. The Data would interact with Business Analysts, Repository , Management and s/Customers of the data. 37. Answer: B. Manager, Data istration. The Manager, Data istration is responsible for planning, organizing, directing and controlling data definition, data use, and ensure data availability for the enterprise. The Data identifies and analyzes information needs for the enterprise or business area, and develops and maintains data architecture plus the strategic data plan. The Metadata is responsible for creating, istrating and enforcing of standards, guidelines and procedures for the use of metadata plus metadata query and analysis tools. 38. Answer: C. Metadata . The Metadata is responsible for creating, istrating and enforcing of standards, guidelines and procedures for the use of metadata plus metadata query and analysis tools. The Manager, Data istration is responsible for planning, organizing, directing and controlling data definition, data use, and ensure data availability for the enterprise. A Database conducts data store modeling, design, implementation, and operation. 39. Answer: D. Provide final review and approval authority over data design for an application system. The Metadata role would: Establish and maintain the metadata architecture; Provide approval authority over metadata policies and design; and Maintain repository security profiles in addition to Metadata tool istration. 40. Answer: C. Metadata Analyst. In a company with a Metadata team, the Metadata Analyst would collect the requirements and design the metadata solution. The Metamodeler would convert the requirements into metamodels. The Metadata is responsible for creating, istrating and enforcing of standards, guidelines and procedures for the use of metadata plus metadata query and analysis tools. The Manager, Data istration is responsible for planning, organizing, directing and controlling data definition, data use, and ensuring data availability for the enterprise. 41. Answer: D. Perform application tuning and performance monitoring. A Database would be responsible for: Establish and maintain sound backup and recovery policies and procedures; Implement and maintain database security (create and maintain s and roles, assign privileges); Perform database tuning and performance Page 39 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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monitoring; Capacity planning; Perform general technical trouble shooting and give consultation to development teams. 42. Answer: B. CEO. The Database would not typically interact with the CEO in an organization. The Database would interact with Business Analysts, Data , Management and s/Customers of the data. 43. Answer: D. Network security istration. When hiring a Database the skill that may be desirable but least preferred in the above list is Network security istration experience. Typically, skills for Database s are: Relational Database; related utilities and tools experience, physical data modeling; ability to perform both Relational Database and Operating System performance tuning and monitoring; and prior work experience. 44. Answer: D. Database . In an organization, the Database would develop the referential integrity constraint scripts. The Data Analyst would work with the Database to link the logical to physical data model. 45. Answer: D. Database . In an organization, the Database would have the responsibility to recover the physical database in the event of a power disruption. 46. Answer: D. Installing the Operating System on the Data Warehouse server. A Data Warehouse would be responsible for: Data Warehouse data modeling and design; Data Warehouse implementation and refresh; Data Access istration; Perform application performance monitoring; Perform general technical trouble shooting and give consultation to development and metadata teams. 47. Answer: B. Ability to calculate Data Warehouse return on investment, costs and benefits. When hiring a Data Warehouse the skill that may be desirable but least preferred in the above list is the ability to calculate Data Warehouse return on investment, costs and benefits. Typically, skills for Data Warehouse s are: Relational Database; related utilities and tools experience, logical and physical data modeling; Expert in data structure including parallel data structure; Extract Transform and Load tool experience; and prior work experience. 48. Answer: A. Business Information Steward. Business Information Steward would review and approve data definitions and domain value specifications for business data. Other responsibilities would include: validating business rules and keeping the domain values current across the Enterprise. Managerial Information Steward is responsible for setting information policy and creating information measures for either the organization or a specific department or business area. Physical Data Trustee is able for the integrity of the physical data assets. An Information Producer is able for the content of the information.

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49. Answer: B. Managerial Information Steward. The Managerial Information Steward has the authority to select and mandate Business Information Stewards. Physical Data Trustee is able for the integrity of the physical data assets. An Information Producer is able for the content of the information. 50. Answer: D. Everyone is responsible for data quality. Everyone in an organization is responsible for data quality. While some roles, like a CEO may not be involved in data entry or usage of transactional systems, they would offer both financial and policy. 51. Answer: A. Creation of data standards. The Physical data trustees are responsible for enforcement of physical security, performance tuning of physical databases, and backup and recovery of physical databases. 52. Answer: B. End . The End would be responsible for creating the data dictionary entries. The Repository would set up the structure and implement the data dictionary. The Data Librarian would assist in cataloguing and categorizing the data. 53. Answer: B. Configuration Management. A new data model is created and rolled into Production. The process that is responsible for ing the modification in the Configuration Management Database (CMDB) is Configuration Management. Configuration and Data Management organizations are responsible for defining, controlling, integrating and implementing essential policies and procedures that provide Configuration Management (CM) and Data Management (DM) discipline on Programs and contracts. Configuration Management Database is a database, which contains all relevant details of each Configuration Item (CI) and details of the important relationships between CIs. 54. Answer: B. Data Model. An example of a Configuration Item is data model. A Configuration item is a component of an infrastructure, or an item associated with infrastructure that needs to be managed and controlled by Configuration Management. 55. Answer: A. Status ing. The disciplines of Data Management Configuration are: Planning, Collection, Approval, Distribution, Storage, and Retrieval of data that are implemented through standard procedures and address specific customer or contractual data management requirements. 56. Answer: A. Management of all documentation and specifications. The only documents that should be managed through Configuration Management are those that relate to: hardware, software, firmware, data model, documentation, test, test fixtures and test documentation of an automated information system, throughout the life of a system.

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2.0 Data & Metadata Infrastructures Creation / Maintenance Overview An overall planning function needs to occur to govern the creation and maintenance of data and metadata infrastructures. The scope of the data and metadata infrastructure includes the definition of all activities and processes involved in the definition, creation, formatting, storage, access and maintenance of data and metadata. Data Architecture Methods defines the process to create infrastructure like using Information Engineering. Information Engineering has many purposes, including organization planning, business re-engineering, application development, information systems planning and systems reengineering. There are a number of data and metadata tools that perform various tasks associated with managing and deploying Data Management that provide flexibility needed to the activities and processes defined in the infrastructure. Data Management tools include Database Management Systems (relational and object), Data modeling tools and Extract, transform, and load (ETL) tools. Metadata tools include Data dictionaries, Data directories, Data encyclopaedias, Metadata registries (e.g. ISO/IEC 11179) and Metadata repositories. The ISO/IEC 11179 Information Technology: Metadata Registries (MDR) specification developed by the ISO (International Standards Organization) and the IEC (the International Electrotechnical Commission) defines a number of fields and relationships for Metadata Registries including a detailed metamodel for defining and ing istered items, of which the primary component is a Data Element. Each data and metadata tool has a different purpose and usage.

Topics Data Architecture Methods Architectures Data Architecture Methods Tools and Technology Types Data . Metadata & Descriptive Information . Data Issues

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Questions 2.1

Planning for Data & Metadata

2.1.1

Architectures

1. Which of the following is not a valid benefit of Enterprise Data Architecture? A. Organizes data around the enterprise's data subjects to create a shared data resource. B. Integrated view of enterprise data. C. Economies of scale in purchasing Case tools. D. Enables organizational change. 2. What is the best answer to why would Enterprise Data Architecture be created? A. Enterprise Data Architecture can be created in one iteration. B. Diagram application-specific databases. C. Information is an asset of the entire organization. D. Design stability and data object abstraction and generalization. 3. Which of the following is not a reason to architect Source Data? A. Determine sources of data needed. B. Determine the index for the data mart. C. Diagram application-specific source data for extraction. D. Determine methods for extraction and delivery. 4. Which one of the following is not a goal of Source Data Architecture? A. Ensure that the source data is extracted only once. B. Define the scope and implementation of the data warehouse. C. Oversee the construction of the enterprise data warehouse. D. Determine the monthly flat file transmission protocol. 5. Which one of the following is targeted towards the efficient delivery of the proper information to the proper recipients? A. Data Sourcing B. Data Distribution C. Data Integration D. Enterprise Data

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6. Which one of the following requires combining and matching information from different sources, and resolving a variety of conflicts? A. Data Sourcing B. Data Distribution C. Data Integration D. Enterprise Data 7. What is a fundamental principle in Change Authorization of Architectures? A. Single point of authorization. B. Single point of access. C. Private key encryption on data. D. Standard for communication. 8. Which of the following is not a feature of the client in client-server architecture? A. ive B. Active C. Sending requests D. Waits until reply arrives 9. Which of the following is not a feature of the server in client-server architecture? A. ive B. Active C. Waiting for requests D. On requests serves them and send a reply 10. What is the best name for a network called if the networks consists clients, application servers which process data for the clients, and database servers, which store data for the application servers? A. 2-tier Architecture B. 3-tier Architecture C. n-tier Architecture D. Multi-tier Architecture 11. Which one of the following is not a source of metadata? A. Case Tools B. Applications C. Physical Database D. Company Directory

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12. Which type of analysis is needed when beginning Metadata solution architecture? A. Metadata record B. Metadata flows C. Metadata Categorization D. Metadata 13. What is best definition of the Zachman Framework? A. A 36-cell Matrix. B. A Normalized schema. C. A good analytical tool. D. Specific to methods/tools.

2.1.2. Data Architecture Methods 14. Which one is not a benefit of Enterprise Architecture Planning? A. Consistency and compatibility of systems. B. Interoperability between systems and databases. C. Economies of scale in purchasing and developing systems. D. Greater ing staff effectiveness. 15. Which one of the following does Enterprise Architecture Planning does not address? A. Data management B. Application environment and development toolsets C. Maintain a secure infrastructure and IT for networks and distributed systems D. Middle-ware and transaction management 16. An Enterprise Architecture Plan is usually part of what overall strategy? A. Information Technology Strategy B. Information Technology Infrastructure Strategy C. Application Infrastructure Plan D. Information Technology Architecture Plan 17. Which one of the following phases is not part of the data life cycle? A. Create/Store B. Modify/Update C. Delete D. Shred

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18. What is the definition of data life cycle management? A. Data Life cycle management is a product. B. Data Life cycle management is an approach to managing an organization's data. C. Data Life cycle describes the CRUD matrix of data elements. D. Data Life cycle is the storage used to store active and inactive data. 19. What is defined as "An integrated and evolutionary set of tasks and techniques that enhance business communication throughout an enterprise enabling it to develop people, procedures and systems to achieve its vision". A. Information Engineering B. Enterprise Architecture Planning C. Data Reengineering D. Prototyping 20. Which one is not a purpose of Information Engineering? A. Organization planning. B. Business re-engineering. C. Application development. D. Data Warehousing. 21. In which situation would a data reengineering apply? A. Developing a migration strategy from one application environment to another. B. Determining active and inactive data. C. Enhance business communication throughout the enterprise. D. Assist in developing the information management strategy. 22. What is the process of quickly putting together a working model in order to test various aspects of the design, illustrate ideas or features and gather early ? A. Information Engineering B. Enterprise Architecture Planning C. Data Reengineering D. Prototyping

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2.2.

Tools and Technology Types

2.2.1. Data 23. What is the definition of a Database Management System (DBMS)? A. Controls the organization, storage and retrieval of data in a database. B. A modeling language to define the schema. C. Inverted list management. D. s the database query language to interactively access data. 24. Which one is not a common DBMS model? A. Hierarchical B. Network C. Relational D. File 25. Which one is not a function of a DBMS? A. A modeling language to define the schema B. A database query language C. Transaction method that ensures Atomicity, Consistency, Isolation, and Durability (ACID) D. RAID Disk arrays. 26. What is the definition of an Object Database Management System (ODBMS)? A. Controls the organization, storage and retrieval of data in a database. B. A modeling language to define the schema C. Inverted list management D. s the database query language to interactively access data. 27. Which one is not a function of an ODBMS? A. Object Definition Language (ODL) B. Object Query Language (OQL) C. C++ and Java Binding. D. Structured Query Language (SQL). 28. Which function does Extract, Transform, and Load (ETL) tool does not involve in the process in data warehousing? A. Extracting data from data sources. B. Transforming data to fit business requirements. C. Transforming metadata to fit business requirements. D. Loading data into the data warehouse. Page 47 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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29. The first part of an ETL process is to extract the data from what type of common data source formats? A. Relational database B. Flat File C. IMS D. C++ 30. Which one is not a typical function of the transformation process in ETL tools? A. Translating code values B. Deriving new calculated values C. ing or merging data from multiple sources D. DDL SQL statements with SQL variations 31. Which of the following is not a type of load function of an ETL tool in the data warehouse? A. Overwrite old information B. Insert new records C. Update old record and Insert new record D. Insert audit trail records. 32. Which of the following is not true of a Data Modeling Tool? A. Specific to a DBMS B. Produce a diagram summarizing the results of your data modeling efforts C. Generate a database schema from a model. D. Diagram of referential integrity constraints.

2.2.2. Metadata & Descriptive Information 33. What is the definition of a data dictionary? A. Set of cleansed, organized, and transaction level data. B. Database that tracks data element definitions. C. Instances of characters. D. Internal database that store information tracked, developed, and maintained by a predefined set of single-vendor tools.

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34. What is the definition of a data dictionary? A. An entity in a file system that contains a group of files. B. LDAP directory services are examples of general-purpose distributed hierarchical object-oriented directory technologies. C. A repository or database of information. D. Directory technology is often used in white page applications and network information services. 35. What is the definition of a data encyclopaedia? A. Set of cleansed, organized, and transaction level data. B. Database that tracks data element definitions. C. Instances of characters. D. Internal database that store information tracked, developed, and maintained by a predefined set of single-vendor tools. 36. What data is not typically held in a data registry? A. Standardized information in a pre-defined model. B. Metadata, system metadata, system engineering C. Reference information D. XML 37. Which one of the following is not a federated Service-Oriented Architecture (SOA) standards-based registry? A. UDDI registry without a repository. B. UDDI registry with a proprietary repository C. ebXML registry-repository D. Combination of UDDI registry and ebXML registry-repository 38. What is defined as an automated resource “used to describe, document, protect, control and access informational representations of an enterprise”. A. Data dictionaries B. Data directories C. Metadata registry D. Metadata repositories

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39. What is “an integrated, virtual holding area with vendor-independent input, access, and structure; used to directly store metadata and/or metadata-based gateways to external metadata”? A. Data dictionaries B. Data directories C. Data encyclopaedias D. Metadata repositories

2.2.3. Data Issues 40. What is the goal of business intelligence tools? A. Detect patterns in data that explain the present and predict the future. B. Visually representing the data. C. Interactive “online” data exploration D. “Slice-and-dice” analysis 41. Which one of the following is not a new way of interacting with data in extended OLAP models? A. Pivot tables B. Small Multiples C. Geospatial Analysis D. Predictive Analytics 42. Which one of the following does not data mining tools have issues in analyzing? A. E-mails. B. Memos C. Marketing material D. Spreadsheet 43. What is a technique to increase searching of unstructured data? A. Data Semantics B. Data Ontologies C. Classification and Taxonomy D. Classes and Relations

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Quick Answers 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

C D B D B C A A B B D D B D C A D B A D A D

23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43.

A D D A D C D D D A B C D D B C D A A D C

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Detailed Answers 1. Answer: C. Economies of scale in purchasing Case tools. The benefits of an Enterprise Data Architecture are that it organizes data around the enterprise’s data subject to create shared data resources, an integrated view of enterprise data that enables organizational change. 2. Answer: D. Design stability and data object abstraction and generalization. An Enterprise Data Architecture should be created for design stability and data object abstraction and generalization. Enterprise Data Architecture treats data like an information asset; it is not application specific but enterprise specific. Enterprise Data Architecture is typically created in more than one iteration. 3. Answer: B. Determine the index for the data mart. The source data should be architected to determine the source of data needed, diagram the source data, and determine the method for extraction and delivery. 4. Answer: D. Determine the monthly flat file transmission protocol. The goal of Source Data Architecture is to ensure that the source data is extracted only once, define the scope and implementation of the data warehouse and oversee the construction of the enterprise data warehouse. 5. Answer: B. Data Distribution. Data Distribution is targeted towards the efficient delivery of the proper information to the proper recipients. In Data Distribution data can be streamed or supplied depending on the requirements of the communication. 6. Answer: C. Data Integration. Data Integration requires combining and matching information in different sources, and resolving a variety of conflicts. XML is becoming a de facto data integration standard. 7. Answer: A. Single point of authorization. A fundamental principle in Change Authorization of Architectures is a single point of change authorization. Every change must run the same process and authorization prior to changes are implemented. 8. Answer: A. ive. In a client-server architecture, the features of the client are: Active (Master), sending request, and waiting until reply arrives. 9. Answer: B. Active. In a client-server architecture, the features of the client are: ive (Slave), waiting for requests, and on requests serves them and send a reply. 10. Answer: B. 3-tier Architecture. If the networks consists of clients, application servers which process data for the clients, and database servers which store data for the application servers it is known as a 3-tier Architecture. 11. Answer: D: Company Directory. Metadata has many sources including but not limited to: Tools, Applications and Software Packages. Page 52 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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12. Answer: D. Metadata . When beginning Metadata solution architecture, the following type of analysis should be carried out: Process Flow, Metadata Flow, Metadata Record Identification, and Metadata Categorization. Metadata occurs at the input and output of processes the tools like case tools and applications gather the metadata. The Metadata Record Identification identifies the data needed in a metadata solution and their origins. The last step is to categorize the metadata for display and usage. 13. Answer: B. A Normalized schema. The Zachman Framework is a normalized schema that is a good analytical tool. It is not a 36-cell matrix. The Zachman Framework logic is neutral to methods and tools. Each cell of the Framework is unique and primitive. 14. Answer: D. Greater ing staff effectiveness. Enterprise Architecture Planning has the following benefits: consistency and compatibility of systems, interoperability between systems and databases, economies of scale in purchasing and developing systems, reduced overall system costs, and greater IT staff effectiveness. 15. Answer: C. Maintain a secure infrastructure and IT for networks and distributed systems. Enterprise Architecture Planning addresses: data management, application environment and development toolsets, middle-ware and transaction management, Web delivery environment, operating systems and other system software, network environment, and hardware server and client environments. 16. Answer: A. Information Technology Strategy. An Enterprise Architecture Plan is usually part of the overall Information Technology Strategy. The Information Technology Strategy leads to Infrastructure Strategy, Information Technology Architecture Plan and Application Infrastructure Plan. 17. Answer: D. Shred. The data life cycle phases are: Create/Store, Retrieve, Modify/Update, Read/Use, Transport, Archive and Delete. The data lifecycle is the process of managing data throughout its lifecycle from conception until disposal, within the constraints of the data policy. 18. Answer: B. Data Life cycle management is an approach to managing an organization's data. The definition of data life cycle management is an approach to managing an organization's data that involves procedures and practices as well as applications. 19. Answer: A. Information Engineering. Information Engineering is defined as "an integrated and evolutionary set of tasks and techniques that enhance business communication throughout an enterprise enabling it to develop people, procedures and systems to achieve its vision". 20. Answer: D. Data Warehousing. Information Engineering has many purposes, including organization planning, business re-engineering, application development, information systems planning and systems re-engineering. Page 53 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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21. Answer: A. Developing a migration strategy from one application environment to another. Data reengineering is structured application redevelopment technique that applies to the structure, function and meaning of data. 22. Answer: D. Prototyping. Prototyping is the process of quickly putting together a working model in order to test various aspects of the design, illustrate ideas or features and gather early . 23. Answer: A. Controls the organization, storage and retrieval of data in a database. A Database Management System controls the organization, storage and retrieval of data in a database. Two of the functions that a DBMS has are a modeling language to define the schema and a database query language. 24. Answer: D. File. The common DBMS models are Hierarchical, Network and Relational. 25. Answer: D. RAID Disk arrays. The functions of a DBMS are: A modeling language to define the schema, a database query language and transaction method that ensures Atomicity, Consistency, Isolation, and Durability (ACID). Many DBMS also the Open Database Connectivity API that s a standard way for programmers to access the DBMS. DBMS do not include a storage device. 26. Answer: A. Controls the organization, storage and retrieval of data in a database. A Database Management System controls the organization, storage and retrieval of data in a database. Two of the functions that a DBMS has are a modeling language to define the schema and a database query language. 27. Answer: D. Structured Query Language. The functions of an ODBMS are: Object Definition Language (ODL), Object Query Language (OQL), C++ and Java Binding. Structured Query Language is a function of a Relational Database Management System. 28. Answer: C. Transforming metadata to fit business requirements. Metadata is created when using ETL tools not manipulated by ETL tools. ETL tools involve the functions of extracting data from data sources, transforming data to fit business requirements and loading data into the data warehouse. 29. Answer: D. C++. The first part of an ETL process is to extract the data from the common data source formats like Relational database, Flat Files, IMS or other data structures such as VSAM or ISAM. Extraction extracts the data into a format for transformation processing. C++ is a programming language not data. 30. Answer: D. DDL SQL statements with SQL variations. Typical functions of the transformation process in ETL tools include: Translating code values (e.g. M for male or F for Female), Deriving new calculated values (A+B = C), ing or merging data from

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multiple sources, generating surrogate key values and summarizing values. The DDL SQL statement with SQL Variations is a DB mechanism to create tables for example. 31. Answer: D. Insert audit trail records. Typical load functions of an ETL tool in a data warehouse are to: Overwrite old information, insert new records and update old record and insert a new record. 32. Answer: A. Specific to a DBMS. Data modeling tools are RDBMS-independent. Data Modeling Tools: Produce a diagram summarizing the results of your data modeling efforts; Generate a database schema from a model; and Diagram of referential integrity constraints. 33. Answer: B. Database that tracks data element definitions. A data dictionary is a database that tracks data element definitions. An encyclopedia is an internal database that store information tracked, developed, and maintained by a predefined set of singlevendor tools. A data staging area is a set of cleansed, organized, and transaction level data. Instances of characters could represent any time of file or data store and is not specific to a data dictionary. 34. Answer: C. A repository or database of information. A data dictionary is a repository or database of information. Data dictionaries can be used as a white page application and network information service or as an LDAP directory service. 35. Answer: D. Internal database that store information tracked, developed, and maintained by a predefined set of single-vendor tools. A data encyclopedia is an Internal database that store information tracked, developed, and maintained by a predefined set of single-vendor tools. A data dictionary is a database that tracks data element definitions. A data staging area is a set of cleansed, organized, and transaction level data. 36. Answer: D. XML. XML is a format. A Data Registry is defined as an automated resource “used to describe, document, protect, control and access informational representations of an enterprise”. Typically the following is held in a data registry: Standardized information in a pre-defined model, Metadata, system metadata, system engineering; and Reference information. Standards for models and templates for data and metadata registries already exist – for example, the ISO 11179 standard for Metadata Registries, and ebXML for XML registries. 37. Answer: B. UDDI registry with a proprietary repository. A federated SOA deployment requires a standards-based registry-repository the choices involve two standards, UDDI and ebXML Registry. A UDDI registry offers a subset of capabilities offered by an ebXML Registry. Published in a UDDI registry are pointers to service artifacts such as WSDL. Published in an ebXML Registry is not just pointers to service artifacts, but also the actual artifact. Thus, an ebXML registry-repository can be used for governance of any type of service artifacts throughout their life cycles.

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38. Answer: C. Metadata registry. A metadata registry is defined as an automated resource “used to describe, document, protect, control and access informational representations of an enterprise”. 39. Answer: D. Metadata repositories. A Metadata repositories are “an integrated, virtual holding area with vendor-independent input, access, and structure; used to directly store metadata and/or metadata-based gateways to external metadata”. 40. Answer: A. Detect patterns in data that explain the present and predict the future. The goal of business intelligence tools are to help end-s detect patterns in data that explain the present and predict the future. End s use a number of techniques to visually represent the data and slice and dice analysis while interactively exploring the data online. 41. Answer: A. Pivot tables. New methods of interacting with data in extended OLAP models are small multiples or multidimensional matrix of related graphs, Geospatial analysis that combines cartographic elements and data information and Predictive Analytics. 42. Answer: D. Spreadsheet. Data mining tools have issues in analyzing unstructured data like e-mails, memos, marketing material, notes from internal groups, news, groups, chats, and whitepapers. 43. Answer: C. Classification and Taxonomy. A technique to increase searching of unstructured data is classification and taxonomy. An Ontology Is “A specification of a representational vocabulary for a shared domain of discourse -- definitions of classes, relations, functions, and other objects -- is called ontology.” – T.R. Gruber

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3.0 Data Analysis and Modeling Overview Planning and requirements analysis are conducted as part of a project life cycle. The scope of the project is typically defined during planning. The scope can be refined using fact finding techniques like interviews or questionnaires. During requirements analysis, the analyst discovers the client’s requirements and detailed information needed to build the application and data model. The requirements are gathered using fact-finding techniques like Surveys, questionnaires, JAD sessions or Legacy systems analysis. The purpose of data modeling is to develop an accurate model, or graphical representation, of the client's data requirements at different levels of abstraction. The data model acts as a framework for the development of the new or enhanced application. There are many data models that may be used in an organization. In a typical organization, the order of creation of data models is Conceptual, Enterprise, Logical, and Physical. There are also Dimensional Data Models that on-line analytical processing applications and Object Oriented Models that structures systems around the data. Regardless of the data models used, they are useful for confirming requirements and leading to development of the application.

Topics Data / Metadata Analysis & Design Fact Finding Techniques Requirements Definition and Management Data Model Components Logical Data Model Dimensional Warehouse Object Oriented / UML Data Representations in Process Models Data / Metadata Model Management Types of Data Models Scope of Model and Metadata Data Model

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Questions 3.1 Data / Metadata Analysis & Design 3.1.1 Fact Finding Techniques 1. What type of fact-finding technique works best when dealing with numerous divisions? A. Interviewing B. Surveys, questionnaires C. JAD session D. Legacy systems analysis 2. What are the benefits of a JAD Workshop? 1. Communication and combined knowledge 2. Build consensus and ownership 3. Improve design quality 4. Design cross-functional solutions A. B. C. D.

1&2 2&3 3&4 All of the above

3. What type of fact-finding technique minimizes time and assists in narrowing scope? A. Interviewing B. Surveys, questionnaires C. JAD session D. Legacy systems analysis 4. What type of fact-finding technique is a systematic attempt to collect information from a person? A. Interviewing B. Surveys, questionnaires C. JAD session D. Legacy systems analysis 5. What type of fact-finding technique is always used in data warehousing projects? A. Interviewing B. Surveys, questionnaires C. JAD session D. Legacy systems analysis

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6. What is the most appropriate type of question to ask in an interview? A. Closed questions B. Open-ended questions C. Leading questions D. None 7. When is an unstructured interview more appropriate than a structured interview? A. When the interviewer wants to gain a broad based view on an issue that needs to be explored. B. Interviewer identifies gaps in the knowledge, which acts as a basis for questions . C. When the interview needs to be goal-oriented. D. It is never appropriate to be unstructured as you always need to be prepared.

3.1.2 Requirements Definition and Management 8. What is the next best step after gathering Requirements for a new system? A. Evaluation of current environment and documentation B. Gap analysis / Future state creation C. Business rules discovery, validation and documentation. D. Data / process matrices creation. 9. What is the next best step after a current state environment evaluation? A. Gap analysis B. Future state creation C. Business rules discovery, validation and documentation. D. Data / process matrices creation. 10. What is the next best step after a current state environment evaluation and future state creation? A. Gap analysis B. Business rules discovery, validation and documentation. C. Data / process matrices creation. D. Requirements tracking and management to implementation 11. Which statement does not describe a business rule? A. It is a statement that defines some facet of the business. B. It asserts business structure, or controls or influences performance of the business. C. It is at the lowest level and cannot be decomposed further or it would lose business meaning. D. It specifies a Pre or Post condition of an entity. Page 59 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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12. Which of the following statements is a business rule? A. When a failure is reported, an expeditor is assigned by the maintenance department who sends the failure form to the service desk for scheduling. B. If Acct_num is between 0 and 5000 then the customer is a member of the branch that may deposit money. C. A customer places an order D. A customer with preferred status should have its orders filled as soon as possible. 13. Which one is not part of a typical business rule creation process? A. Discovery, B. Validation C. Documentation D. Rule Engine 14. Which of the following is not an example language to express business rules? A. UML B. Z notation C. BPEL D. ABAP 15. What is the benefit of Requirements tracking and management to implementation? A. To provide a matrix that has a listing of the requirements for the entire project. B. To ensure the system performs as it should. C. The information in the matrix includes the number assigned to the requirement, a brief description, the date submitted to project, and the tracking of the requirement as it relates to development. D. Determines multi-modal requirements. 16. What is the best benefit of a Data / process matrix? A. The matrix is used to represent relationships between data and process. B. Shorten design lead times while maintaining product quality. C. To clarify dependencies of information flow. D. To document the meaningful relationships between both process and data as it was related to the authors during the interviews.

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3.2 Data Model Components 3.2.1 Logical Data Modeling 17. What are the major components in an Entity-Relationship diagram? A. Attributes, relationships, and associations; B. Object types, relationships, associations, and supertype/subtypes; C. Object types and transitions, associations, and supertype/subtypes; D. States and transitions; 18. What does the following Entity-relationship diagram describe? Negotiates Price

Buyer

Agent

Seller 1. Real estate agent negotiates price between buyer and seller. 2. Buyer negotiates price with seller, through real estate agent A. B. C. D.

1. 2. 1&2 Neither 1 or 2

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19. In the following diagram, what are the subtypes? Employee

Manager

1. 2. 3. 4. A. B. C. D.

Contract Employee

Hourly Employee

Employee Manager Contract Employee Hourly Employee

1 2 3&4 2, 3 & 4

20. What is the definition of an Attribute? A. An atomic fact or characteristic, which describes an entity. B. A description of an entity occurrence. C. A decomposable fact or characteristic, which describes an entity. D. An association between entities. 21. Attributes roles do the following: a. Identify an occurrence of an entity type (primary key) b. Relate an occurrence of one entity type to an occurrence of another entity type (foreign key) c. Describe the entity or association d. Derives values from other data values in the model. A. B. C. D.

1&2 1&3 1, 2, & 3 1, 2, 3, & 4

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22. What is the definition of cardinality? A. Relate an occurrence of one entity type to an occurrence of another entity type; B. The relative number of occurrences which may exist between a pair of entities; C. A characteristic that describes something about an entity; D. Identify an occurrence of an entity type;

23. What is a Mandatory relationship in Optionality? A. At least one or many; B. None, one, or many C. One and only one D. None or one 24. Describe the following data model: Employee works for employs

A. B. C. D.

Organization Unit

Each Employee “works-for” one and only one Organization Unit Each Employee “works-for” at least one or many Organization Unit Each Employee “works-for” none, one or many Organization Unit Each Employee “works-for” none or one Organization Unit

25. What is the relationship between the Primary Key and Foreign Keys? A. One-to-one; B. One-to-many; C. Many-to-Many; D. Foreign Keys do not relate entities; 26. What is the Attribute that uniquely identifies an entity is called? A. Entity Type B. Entity Occurrence C. Primary Key D. Foreign Key

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27. What is the difference between an Entity Type and Entity Occurrence? A. An Entity Type is something that exists and is capable of being described and an entity occurrence is a relationship; B. An Entity Type is the definition and the entity occurrence is an instance of the Entity; C. An Entity Type is a physical object type and the entity occurrence is the project. D. There is no difference. 28. What data model is the Normalization Process applied? A. Conceptual Data Model B. Logical Data Model C. Physical Data Model D. Metadata Data Model 29. What is the objective of the Normalization process? A. To identify the one best place an attribute belongs. B. To organize the physical design of the data model into tables and columns. C. To organize columns based on the mathematical principles of set theory. D. To assign an attribute to multiple entities. 30. What are the attributes of a data model in First Normal Form? A. All repeating groups have been eliminated B. Every attribute describes completely that entity and not an entity identified by only part of the primary identifier. C. Data items that do not describe the entire primary key of the entity are eliminated. D. Identified restrictions that apply to the data and its relationships. 31. What are the attributes of a data model in Second Normal Form? A. All repeating groups have been eliminated B. Every attribute describes completely that entity and not an entity identified by only part of the primary identifier. C. Data items that do not describe the entire primary key of the entity are eliminated. D. Identified restrictions that apply to the data and its relationships. 32. What are the attributes of a data model in Third Normal Form? A. All repeating groups have been eliminated B. Every attribute describes completely that entity and not an entity identified by only part of the primary identifier. C. Data items that do not describe the entire primary key of the entity are eliminated. D. Identified restrictions that apply to the data and its relationships.

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33. What are the two principle types of static relationships in a class diagram? A. Primary Key and Foreign Key. B. Association and Subtype. C. Cardinality and Optionality. D. One-to-One and One-to-Many.

3.2.2 Dimensional Warehouse 34. When using Dimensional Modeling, a logical design technique, data is presented in which manner? A. Processes, data stores, flows and terminators. B. Processes, data stores, relationships and flows. C. Fact tables and dimension tables. D. Entities, data, and relationships. 35. Which one of the following is best described as a fact: A. promotion_name B. clerk_grade C. address D. dollars_sold 36. In Dimensional Modeling, snowflaking is a technique that does the following: A. Aggregates fact tables into summary tables for easier querying. B. Adding more attributes into a single dimension table to make it easier for the business understandability. C. Removes low-cardinality textual attributes from dimension tables and places them in ed “secondary” dimension table. D. Identifies cross-dimensional attributes for easier browsing performance. 37. Which application is the best fit for Dimensional Modeling? A. OLTP B. OLAP C. HPC D. Web

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38. In a Dimensional Model, when tracking changes in a slowly changing dimensional table, the old value is discarded and has no significance is regarded as what type? A. type 1 B. type 2 C. type 3 D. cross-dimensional attribute 39. In a Dimensional Model, when tracking changes in a slowly changing dimensional table, the old value is recorded and has significance is regarded as what type? A. type 1 B. type 2 C. type 3 D. cross-dimensional attribute 40. In a Dimensional Model, when tracking changes in a slowly changing dimensional table, the old value and new value are equally important is regarded as what type? A. type 1 B. type 2 C. type 3 D. cross-dimensional attribute 41. In the Customer Dimension, a Slowly Changing Type 2 Dimension, the key would be described as the following? A. Customer_Key which is a new generated, meaningless, key; B. Customer_Key – made of Customer_Number appended with Time_Key; C. Customer_Key – made of Customer_Number appended with version number; D. Customer_Key from the old record because it will be overwritten. E. Customer_Key – made of the Operational Key

3.2.3 Object Oriented / UML 42. What term in the basis of modularity and structure in object-oriented modeling? A. Entity B. Class C. Object D. Relationship.

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43. What is the definition of an object? A. An instance of a class. B. Behavior that varies depending on the class. C. A type of privacy applied to the data and some of the methods. D. A person, place or thing. 44. What is the term for a type of privacy applied to the data and some methods of a class? A. Encapsulation B. Inheritance C. Abstractions D. Polymorphism 45. When modeling a class diagram, what is an association? A. An association represents relationship between instances of classes. B. A state of being associated. C. Primary and Foreign Key relationship between objects. D. There is no concept of an association in a class diagram. 46. Which one is not one of the relationship types in UML Modeling? A. Generalization B. Association C. Aggregation, Composition D. Polymorphism 47. What is the term for how many objects may participate in a given relationship in a class diagram? A. Cardinality B. Optionality C. Multiplicity D. Pairing 48. In the following diagram, what does the association show in of multiplicity? 1 A. B. C. D.

Exactly one. Many. Optional. Number specified.

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49. In the following diagram, what does the association show in of multiplicity? * A. B. C. D.

Exactly one. Many. Optional. Number specified

50. In the following diagram, what does the association show in of multiplicity? 0..1 A. B. C. D.

Exactly one. Many. Optional. Number specified

51. In the following diagram, what does the association show in of multiplicity? 1..10 A. B. C. D.

Exactly one. Many (zero or more) Optional (zero or one) Number specified

52. What Association does the following diagram represent?

A. B. C. D.

Aggregation. Composition. Ordered Role. Not an association type.

53. What does the marking + on an attribute mean? A. Public attribute B. Protected attribute C. Private attribute D. Package attribute

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54. Which of the following is false in describing Attributes in UML? A. Attributes are always single-valued. B. Attributes may be optional or mandatory C. Optional or mandatory may be drawn on the class diagram. D. Attributes have name, type, and default values. 55. What is the term that defines the processes that a class can carry out? A. Operations. B. Specifications. C. Multiplicity. D. Notation.

3.2.4 Data Representations in Process Models 56. Which one of the following is not a true statement about triggers? A. Triggers can accept parameters B. Triggers are code that automatically execute on a table or data in response to certain events. C. Triggers can be used to enforce Referential Integrity between tables. D. Triggers can be executed after SQL command type statements of: INSERT, UPDATE, or DELETE. 57. Which one of the following is not a typical type of referential integrity trigger? A. Identifying B. Non-identifying C. Subtype D. Supertype 58. What is the difference between a stored procedure and a trigger? A. Stored procedures can accept parameters while Triggers cannot. B. Stored procedures are stored in the database while Triggers are not. C. Stored procedures and procedural while Triggers can be used to enforce Referential Integrity between tables. D. Stored procedures and Triggers can simplify data management. 59. What is the benefit of the Unified Modeling Language (UML)? A. UML standardizes representation of object oriented analysis and design. B. In UML, requirements gathering comprise of Use Case and Activity diagrams. C. In UML, design comprise Class and Object diagrams. D. In UML, deployment comprises of Package and Subsystem diagrams.

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3.3 Data / Metadata Model Management 3.3.2 Types of Data Models 60. What data model is composed of subject areas, relationships, and subject area definitions? A. Conceptual Data Model B. Logical Data Model C. Physical Data Model D. Dimensional Data Model 61. What is the difference between a Conceptual Data Model and Enterprise Data Model? A. A Conceptual Data Model is a type of Business Model while an Enterprise Data Model is a physical data model. B. A Conceptual Data Model describes the whole enterprise business subject areas while an Enterprise Data Model is a decomposition of subject area entities. C. A Conceptual Data Model is a logical data model while an Enterprise Data model is a physical data model. D. A Conceptual Data Model is a concept applied to the enterprise while an Enterprise Data Model is applied to databases. 62. In a typical organization, the order of creation of data models is in which of the following orders? A. Conceptual, Enterprise, Logical, Physical B. Enterprise, Conceptual, Logical, Physical C. Logical, Conceptual, Enterprise, Physical D. Physical, Logical, Enterprise, Conceptual 63. What data model is composed of tables and columns? A. Conceptual Data Model B. Logical Data Model C. Physical Data Model D. Dimensional Data Model 64. What data model is geared to a decision environment? A. Conceptual Data Model B. Logical Data Model C. Physical Data Model D. Dimensional Data Model

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65. Which data model has an Enterprise Wide scope? A. Conceptual Data Model B. Logical Data Model C. Physical Data Model D. Dimensional Data Model 66. Which data model has all the entities for each subject area? A. Logical Data Model B. Physical Data Model C. Dimensional Data Model D. Enterprise Data Model 67. What are the elements in an Enterprise Data Model? A. Use Cases and Activity diagrams B. Entities and Relationships C. Class and Object Diagrams D. Tables and Relationships 68. What is the definition of a metamodel? A. Data models that specify one or more other data models. B. Models of unclassified metadata. C. Physical metadata storage. D. Models that describe the flow of metadata. 69. What is the difference between a metamodel and a meta-metamodel? A. Metamodels are data models that specify other data models, while metametamodels defines ontology. B. Metamodels are the definition of concepts while meta-metamodels defines the language. C. Metamodels describe the types of records, while meta-metamodels describe the type of records used in the database. D. There is no difference. 70. Which one of the following is not an industry standard? A. Case Data Interexchange Format (CDIF) B. Meta Data Coalition (MDC) C. Common Warehouse Model (CWM) D. Metadata Interchange Specification (MDIS)

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71. What is the benefit of universal / industry models? A. Standard data models for an industry that may be used off the shelf. B. Adapt to industry s' characteristics. C. The data requirements have already been gathered and provided in use cases. D. There is no benefit as customizations can take hundreds of hours. 72. Which one of the following phases is not part of the data life cycle? A. B. C. D.

Create/Store Modify/Update Delete Shred

3.2.2 Scope of Model and Metadata 73. Which of the following is not true about the scope of an enterprise wide data model? A. s a wide audience. B. Provides a data picture of the business. C. Causes internal gridlock and inconsistencies. D. Capable of being easily extended to capture new requirements. 74. What is the scope of the enterprise wide data model? A. Business Unit (i.e. Marketing) B. Corporation C. Geographic Unit (i.e. North America) D. Functional Area (Information Technology) 75. Which statement is not true about the enterprise wide data model? A. The corporate data architect owns the Enterprise wide data model. B. The Enterprise wide data model is driven by the business. C. Subject areas are areas of concern for the corporation. D. The enterprise data model will frequently change. 76. A data model represents Marketing data for an organization. What is scope of data model? A. Enterprise B. Business Area C. Project Oriented D. Subject Area

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77. A data model represents financial data for an organization. What is scope of data model? A. Enterprise B. Business Area C. Project Oriented D. Subject Area

78. A project has begun to track costs for starting up operations in Plant A. scope of data model? A. Enterprise B. Business Area C. Project Oriented D. Subject Area

What is

3.2.3 Data Model 79. When using a data modeling tool, which one of the following is not applicable when creating data models? A. Forward Engineering B. Reverse Engineering C. Create Logical and Physical data models D. Split models of older versions into separate logical and physical 80. What is creating a data model from an existing database is known as? A. Forward Engineering B. Reverse Engineering C. Create Logical and Physical data models D. Importing prior version data model 81. Which one of the following is not a benefit of Reverse Engineering Functionality? A. Maintain database B. Change Database Type (e.g. from SQLServer to Oracle ) C. Analyze differences in databases D. Create the Enterprise Data Model

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82. Which one is not a typical Data Model tool function? A. Forward Engineering B. Reverse Engineering C. Comparisons D. Improving 83. What is the benefit of the Comparisons in data modeling tools? A. Keep data model and database synchronized. B. Compare changes between data model and database. C. Select objects that want to compare. D. Selectively import or export changes. 84. The database would like to create a database from an existing data model, which data modeling tool function would they use? A. Forward Engineering B. Reverse Engineering C. Comparisons D. Version Control 85. When a data modeler would like to roll back a change to a data model, which function they would use? A. Change Control B. Model Merge C. Versioning D. Submodeling 86. When a data modeler would like to create an enterprise model which data model function would be used? A. Change Control B. Model Merge C. Versioning D. Submodeling

87. Which of the following is not a reason for using Model Merge feature? A. To create an enterprise data model. B. To bring individual data models together in a group. C. Two previously unrelated projects have merged. D. Comparing two data models to detect changes.

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88. Which one of the following does not apply when importing Customer data model with New Customer data model? A. Create a new data model B. Merge New Customer into Customer data model C. Merge Customer into New Customer data model D. The functionality is not allowed. 89. Which one is not typically represented in the breadth of data models in data modeling tools? A. Enterprise Data Model B. Logical Data Model C. Physical Data Model D. Business Process Model 90. What is the benefit of linkages and mappings between enterprise, logical, and physical data models? A. To define the different purposes of the data models in the application development process. B. To maintain links between the different data models. C. Synchronize changes between data models. D. Applying transformation functions to the data models.

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Quick Answers 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

C D A A D B A A B A D D D D B C B C D A D B A

24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46.

A B C B B A A B C B C D C B A A A A B A A A D

47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69.

C A B C D A A C A A D A A A B A C D A D B A A

70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90.

B A D C B D B D C A B D D A A C B D D D C

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Detailed Answers 1. Answer: C. JAD Session: t Application Design session is a method for performing analysis that brings specific parties together within a workshop environment. Surveys and questionnaires must be made up of closed questions and typically involve clarification and discussion after the results are tabulated. Interviews are best done in small groups. Legacy system analysis involves examining and probing the legacy systems. 2. Answer: D. All of the above are the benefits of a JAD Workshop. 3. Answer: A. Surveys, questionnaires. Surveys and questionnaire is an effective technique to get opinions from a wide variety of stakeholders in an organization. Surveys and questionnaires must be made up of closed questions and typically involve clarification and discussion after the results are tabulated. t Application Design session is a method for performing analysis that brings specific parties together within a workshop environment to collect requirements. Interviews are best done in small groups and can be used when scope is unknown. Legacy system analysis involves examining and probing the legacy systems. 4. Answer: A. Interviewing. Surveys and questionnaire are typically done electronically. t Application Design is a workshop environment to collect requirements. Interviews are best done in small groups where heuristic questions can be asked. Legacy system analysis involves examining and probing the legacy systems and may or may not involve discussion with staff. 5. Answer: D. Legacy systems analysis. Legacy system analysis is used in data warehousing projects to perform source(legacy system) to target(data warehouse) transformations. 6. Answer: B. Open-ended questions. Open-ended questions cannot be answered with a simple yes or no response and thus encourage the interviewee to provide more information. Closed-ended questions give a yes or no answer. Leading questions put the interviewee’s opinion into the question and do not give an opportunity for the interviewer to answer without bias. 7. Answer: A. When the interviewer wants to gain a broad based view on an issue that needs to be explored. The unstructured interview is used when the interviewer wants to explore an issue and facilitates description of domain in a way that is easy for the interviewee. 8. Answer: A. Evaluation of current environment and documentation. After gathering requirements for a new system, the next best step is an evaluation of the current environment and documentation to complete the current state assessment. Next, a future state can be derived with the linkages to the current state if needed. A gap analysis is

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then conducted to compare its current state with its future state to determine the variance between business requirements and current capabilities. 9. Answer: B. Future state. After creating a current state environment evaluation, the next best step is to complete the future state assessment. Next, a gap analysis is then conducted to compare its current state with its future state to determine the variance between business requirements and current capabilities. 10. Answer: A. Gap analysis. After creating a current state environment evaluation and future state creation, the next best step is to complete the gap analysis. A gap analysis compares current state with future state to determine the variance between business requirements and current capabilities. 11. Answer: D. It specifies a Pre or Post condition of an entity. Business rules are put in business not in of conditions on entities. A business rule: • Is a statement that defines some facet of the business. • Asserts business structure or controls or influences performance of the business and can be applied across the organization. • Is at the lowest level and cannot be decomposed further or it would lose business meaning. 12. Answer: D. A customer with preferred status should have its orders filled as soon as possible. Business rules should contain the words: must; must not; should; should not; only; only if. The following did not qualify as business rules due to the following reasons: When a failure is reported, an expeditor is assigned by the maintenance department who sends the failure form to the service desk for scheduling. Ordering of events in the business rule is declarative. Business rules should be procedural. If Acct_num is between 0 and 5000 then the customer is a member of the branch that may deposit money. Business rules should not contain technology nomenclature but be solely about the business. A customer places an order. This rule can be decomposed into further rules. 13. Answer: D. Rule Engine. The typical business rule creation process is discovery, validation and documentation. Business rules are discovered as part of a formal requirement gathering process during the initial stages of design. Once they are documented, they are validated to ensure consistency and non-conflicting business rules. Finally, they are documented. In some organizations, software packages are used to store business rules. 14. Answer: D. ABAP. ABAP is a language used in SAP. If the situation merits, business rules can be expressed in a very formal language, for example: UML, Z notation, Business Rules Markup Language, Business Process Execution Language (BPEL) and Business Process Modeling Notation (BPMN).

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15. Answer: B. To ensure the system performs as it should. The benefit of Requirements tracking and management to implementation is to ensure the system performs as it should. The requirements should be classified in a matrix that has a listing of the requirements for the entire project. The type of information about each requirement in the matrix should consist of the following information: the number assigned to the requirement, a brief description, the date submitted to project, and the tracking of the requirement as it relates to development. The matrix can determine multi-modal requirements. 16. Answer: C. To clarify dependencies of information flow. The matrix has the following structure: the columns in the matrix represent the major processes, and the rows the Entities and Attributes. Elements of the matrix thus represent interactions between a Process and Data. Matrix elements marked with an X represent data/process interactions. 17. Answer: B. Entity-relationship diagrams consist of two major components: Object types and relationships. Object types represent a collection, or set, or objects that can be identified uniquely, and can be described by one or more facts (represented by a rectangular box). Relationships represent a set of connections, or associates, between the object types (represented by diamonds). Associations represent a relationship that we need to maintain information. The subtype/supertype indicator consists of an object type and one or more subcategories; connected by a relationship. 18. Answer: C. The diagram shows that both descriptions are valid. 19. Answer: D. Manager, Contract Employee and Hourly Employee are examples of subtypes. Employee is the general category and the subcategories are: Manager, Contract Employee and hourly employee. 20. Answer: A. Attributes are an atomic fact or characteristic, which describes an entity. It possible to differentiate between an entity and attribute by examining whether it can stand-alone and hold meaning. For example Street Name only makes sense when it resides in the context of Employee. Street Name is an attribute of Employee entity an as such modifies Employee. Employee is an entity. One occurrence of Employee entity might be the employee Johnson. Attributes such as Street Name, City, and Phone Number are attributes, which describe or modify Employee Johnson. 21. Answer: D: 1, 2, 3, & 4 Attributes roles identify, describe and relate attributes. 22. Answer: B. Cardinality can be defined as the relative number of occurrences, which may exist between a pair of entities. There are three kinds of relationships: one-to-one; one-to-many; and many-to-many. In a one-to-one relationship between two entities, at any one time there exists only one occurrence of the entity (“Customer” and “Employee”). In a one-to-many relationship between two entities, at any one time, there may exist multiple occurrences of the entity “Customer” for each entity of “Customer

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Type”. In a many-to-many relationship between two entities, multiple occurrences of the entity “Employee” can exist for multiple occurrences of the entity “Skill”. 23. Answer: A. Mandatory means At least one or many and is denoted by a bar. 24. Answer: A. The Data Model describes the relationship that Each Employee “worksfor” one and only one Organization Unit. The model also states that Each Organization Unit can have many employees working for it. Bar indicates a Mandatory relationship between Employee and Organization Unit. The Circle indicates an Optional relationship between Organization Unit and Employee. 25. Answer: B. One-to-many. The primary key of the “one” entity becomes the foreign key of the “many” entity. Foreign key data columns are part of the “many” entity in a one-to-many relationship that is identified through the Normalization Process. 26. Answer: C. Primary Key. The Attribute that uniquely identifies an entity is called a Primary Key. Foreign key data columns are part of the “many” entity in a one-to-many relationship that is identified through the Normalization Process. An Entity Type is the definition and the entity occurrence is an instance of the Entity. An entity type is an identifiable person, organization, place, event, concept or thing that exists and is capable of being described like an Employee. An Entity Occurrence is a unique instance of the entity type like one Employee may have the last name of Johnson, Smith, and Lee. 27. Answer: B. An Entity Type is the definition and the entity occurrence is an instance of the Entity. An entity type is an identifiable person, organization, place, event, concept or thing that exists and is capable of being described like an Employee. An Entity Occurrence is a unique instance of the entity type like one Employee may have the last name of Johnson, Smith, and Lee. 28. Answer: B. Logical Data Model. Normalizing the Logical Data Model to the generally accepted 3rd normal form is required to make the model useful for translation into a physical model that can be implemented with little redundancy and remove potential data anomalies in the create, update and deletion of data. The Normalization process is not applied to the conceptual, physical or metadata data models. 29. Answer: A. To identify the one best place an attribute belongs. The process of normalizing data elements is a technique based on mathematical principles of set theory, first introduced by Dr. E. F. Codd. Normalization is a systematic process of grouping attributes into data entities. Normalization does not dictate any physical design like tables or columns. Normalization finds the one best place that an attributes belongs not multiple places. 30. Answer: A. All repeating groups have been eliminated. A data model in First Normal Form has all repeating groups eliminated. A data model in Second Normal Form has every attribute describes completely that entity and not an entity identified by only part of the primary identifier. A data model in Third Normal Form has data items that do not Page 80 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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describe the entire primary key of the entity are eliminated. A simple rhyme to the ordering is "the key, the whole key and nothing but the key", so help me Codd. 31. Answer: B. Every attribute describes completely that entity and not an entity identified by only part of the primary identifier. A data model in First Normal Form has all repeating groups eliminated. A data model in Second Normal Form has every attribute describes completely that entity and not an entity identified by only part of the primary identifier. A data model in Third Normal Form has data items that do not describe the entire primary key of the entity are eliminated. A simple rhyme to the ordering is "the key, the whole key and nothing but the key", so help me Codd. 32. Answer: C. Data items that do not describe the entire primary key of the entity are eliminated. A data model in First Normal Form has all repeating groups eliminated. A data model in Second Normal Form has every attribute describes completely that entity and not an entity identified by only part of the primary identifier. A data model in Third Normal Form has data items that do not describe the entire primary key of the entity are eliminated. A simple rhyme to the ordering is "the key, the whole key and nothing but the key", so help me Codd. 33. Answer: B. Association and Subtype. The two principle types of static relationships in a class diagram are association and subtype. Primary key and foreign key, cardinality and optionality, and one-to-one and one-to-many describe data models. 34. Answer: C. Fact tables and dimension tables. Every dimensional model is composed of one table with a multipart key called a fact table and a set of tables called dimension tables that describe the dimensions of the fact table. Examples of dimension tables are: Time, Store, Product, Customer, and Employee while the fact table could be: Sales. The Data Warehouse Bus Architecture may be defined as: A master suite of conformed dimensions and to standardize the definitions of facts. Process, data stores, flows and terminators are part of data flow diagrams. Entities, data and relationships are part of data modeling. Processes, data stores, relationships and flows are a combination of data flow diagrams and data modeling. 35. Answer: D. dollars_sold is a fact attribute. The most useful facts in a fact table are numeric, additive and continuously valued. Continuously valued means that every time the attribute is sampled, it can take on different values. Dimension tables, most often contain descriptive textual information. Clerk_grade, address and promotion_name would all be dimensions. 36. Answer: C. Snowflaking removes low-cardinality textual attributes from dimension tables and places them in ed “secondary” dimension table. Snowflaking may be used both logically and physically. For example Customer may be separated from Customer Type in two dimension tables. 37. Answer: B. OLAP. Dimensional Modeling is best used in OLAP type applications for browsing, performance, and understandability. OLTP systems or online Page 81 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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transaction processing systems use a normalized data model. HPC or High Performance Computing uses a normalized model that typically resides in memory for faster transaction time. Web is similar to OLTP in the data model used. 38. Answer: A. type 1. In a Dimensional Model, when tracking changes in a slowly changing dimensional table, the old value is discarded and has no significance is regarded as type 1. Type 1 overwrites the old record and does not track changes. Type 2 tracks full changes and partitions history of the dimension table. Type 3 tracks old and new definitions on the same record. Cross-dimensional attribute is an attribute that describes an attribute but may also be counted and could reside in either a fact or dimension table. 39. Answer: A. type 2. In a Dimensional Model, when tracking changes in a slowly changing dimensional table, the old value is recorded and has significance is regarded as type 2. Type 2 tracks full changes and partitions history of the dimension table. Type 1 overwrites the old record and does not track changes. Type 3 tracks old and new definitions on the same record. Cross-dimensional attribute is an attribute that describes an attribute but may also be counted and could reside in either a fact or dimension table. 40. Answer: A. type 3. In a Dimensional Model, when tracking changes in a slowly changing dimensional table, the old value and new value are equally important is regarded as type 3. Type 1 overwrites the old record and does not track changes. Type 2 tracks full changes and partitions history of the dimension table. Type 3 tracks old and new definitions on the same record. Cross-dimensional attribute is an attribute that describes an attribute but may also be counted and could reside in either a fact or dimension table. 41. Answer: A. In the Customer Dimension, a Slowly Changing Type 2 Dimension, the key would be Customer_Key, which is a new generated, meaningless, key or surrogate key. The type 2 response requires the use of a surrogate key to fully track the changes of the record. Type One Dimension is overwritten. Intelligence in the key like Time or Version numbers should not be used when creating keys. 42. Answer: B. Class. The basis of modularity and structure in object-oriented modeling is called class. A class is a grouping of data and behaviour for a concept. Entity and Relationship are concepts from data modeling not object-oriented modeling. An object is an instance of a class. Each object has its own data, though the code within a class. 43. Answer: A. An instance of a class. An object is an instance of a class. Each object has its own data, though the code within a class. 44. Answer: A. Encapsulation. The term for a type of privacy applied to the data and some methods of a call is known as encapsulation. Inheritance is a method of generalization that creates subtypes. Abstraction is the ability of a function to have different specifications. Polymorphism is the ability of objects belonging to different types to respond to method calls of methods of the same name, each one according to the right type-specific behavior. Page 82 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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45. Answer: A. An association represents relationship between instances of classes. When modeling a class diagram, an association represents the relationship between instances of classes. Each relationship has two roles, both to and from each class. 46. Answer: D. Polymorphism. The relationship types in UML Modeling are Generalization, Association and Aggregation, Composition. Generalization typifies an inheritance relationship of subtype/supertype. Association represents the relationships between objects. Aggregation and Composition are a special type of association that specifies a HAS-A relationship. Polymorphism is the ability of objects belonging to different types to respond to method calls of methods of the same name, each one according to the right type-specific behavior. 47. Answer: C. Multiplicity. The term for how many objects that may participate in a given relationship in a class diagram is multiplicity. Cardinality and Optionality are in that describe participation in a relationship(optional, mandatory, etc.) and the number of times the entities can participate (one-to-one). 48. Answer: A. Exactly one. The number one specifies exactly one. 49. Answer: B. Many (zero or more). The * defines many (zero or more). 50. Answer: C. Optional. The diagram refers to a many (zero or one) association. 51. Answer: D. Number specified. 1..10 specify the number of items in multiplicity. 52. Answer: A. Aggregation. The diagram represents an aggregation association. A composition association would have a dark diamond. The ordered role is notated by a star. 53. Answer: A. Public attribute. The marking + on an attribute means it is a public attribute. The following is a listing for all the markings: "+" for public "#" for protected "−" for private "~" for package. 54. Answer: C. Optional or mandatory may be drawn on the class diagram. A diagram cannot specify optional or mandatory attributes on a class diagram. 55. Answer: A. Operations. The term that defines what processes a class can carry out is Operations. Operations that change the state of an object are called modifiers. Operations and modifiers are typically used interchangeably. 56. Answer: A. Triggers can accept parameters. Triggers are code that automatically execute on a table or data in response to certain events. Triggers can be used to enforce Page 83 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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referential integrity between tables that have a relationship. There are typically three triggering EVENTS that cause trigger to 'fire': • INSERT event (as a new record is being inserted into the database). • UPDATE event (as a record is being changed). • DELETE event (as a record is being deleted). 57. Answer: D. Supertype. The following are typical types of referential integrity triggers: Identifying, non-identifying (allowing nulls and non-null), and subtype. The typical actions that the triggers can conduct are: CASCADE, RESTRICT, SET NULL (non-identifying-nulls allowed), SET DEFAULT (non-identifying), and NONE. 58. Answer: A. Stored procedures can accept parameters while Triggers cannot. Stored Procedure is a program which, like a Trigger is physically stored in a database. A stored procedure and trigger can both be used to enforce Referential Integrity and simply data management. 59. Answer: A. UML standardizes representation of object oriented analysis and design. While all of the other statements are true, the overall benefit of UML is that it standardizes representation of the object oriented analysis and design. 60. Answer: A. A conceptual model is composed of subject areas, relationships, and subject area definitions. A Conceptual Model is a type of Business Model. 61. Answer: B. A Conceptual Data Model describes the whole enterprise business subject areas while an Enterprise Data Model is a decomposition of subject area entities. A Conceptual Model is a high-level starting-point for design and construction activities leading to implemented information systems that fulfill important business needs. The Enterprise Data Model is at a lower level of detail than the conceptual model. A typical conceptual model of a whole enterprise might consist of 7-9 subject areas, representing major business subject areas. An Enterprise Data Model (EDM) while still not containing all entities or all relationship will have many entities for each subject area. 62. Answer: A. Conceptual, Enterprise, Logical, Physical. In a typical organization, the order of creation of data models is Conceptual, Enterprise, Logical, Physical. 63. Answer: C. A Physical data model is composed of tables and columns. Physical data models are representations of models that specify database or file structure implementations. 64. Answer: D. A Dimension Data Model is geared to decision environments. Data is modeled for retrieval of large amounts of data. Design for high volume retrieval is coupled with specialized istration skills and techniques, and often specialized dimensional database management systems. 65. Answer: A. Conceptual Data Model. A Conceptual Data Model describes the whole enterprise business subject areas.

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66. Answer: D. Enterprise Data Model. An Enterprise Data Model (EDM) while still not containing all entities or all relationship will have many entities for each subject area. 67. Answer: B. Entities and Relationships. An Enterprise Data Model is comprised of Entities and Relationships that are organized into Subject Areas. The model may include attributes of the Entities. 68. Answer: A. Data models that specify one or more other data models. Metamodels are the details behind the metadata that depict metadata relationships. 69. Answer: A. Metamodels are data models that specify other data models, while metametamodels defines ontology. 70. Answer: B. Meta Data Coalition (MDC). Meta Data Coalition is a group that defined the Metadata Interchange Specification. CDIF, CWM, MDIS are all industry standards. 71. Answer: A. Standard data models for an industry that may be used off the shelf. Standard data models are widely used in an industry and shared among different companies. 72. Answer: D. Shred. The data life cycle phases are: Create/Store, Retrieve, Modify/Update, Read/Use, Transport, Archive and Delete. The data lifecycle is the process of managing data throughout its lifecycle from conception until disposal, within the constraints of the data policy. 73. Answer: C. Causes internal gridlock and inconsistencies. The Enterprise wide data model known as a Conceptual Data Model describes the whole enterprise business subject areas, s the entire enterprise, a wide audience, provides a data picture of the business that is capable of being easily extended to capture new requirements. The Enterprise wide data model leads to and integrated data picture that breaks down inconsistencies and promotes data knowledge, use and sharing. 74. Answer: B. Corporation. The Enterprise wide data model, known as the Conceptual Data Model describes the whole corporation not just a business unit, geographic unit or functional area. 75. Answer: D. The enterprise data model will frequently change as new requirements are determined. The enterprise data model should remain stable unless for example, a new company is acquired. The enterprise wide data model is driven by the business, which encomes areas of concern or importance to the corporation. The corporate data architect owns the enterprise wide data model. 76. Answer: B. Business Area. The scope of a data model that represents Marketing data for an organization is business area. The data model may be specific to Marketing area needs or may have access restricted to the Marketing group. The data model would be a part of the enterprise data model. Page 85 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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77. Answer: D. Subject Area. The scope of a data model that represents financial data for an organization is subject area. Every area in the organization needs financial information of some sort, not just the Finance group. The Finance group will see the financial data for an entire organization. 78. Answer: C. Project Oriented. The scope of a data model that tracks costs for starting up operations in Plant A is Project Oriented. The data will only need to be collected while the project is in progress, and analyzed after the closure of the project. 79. Answer: A. Forward Engineering. Forward Engineering the data model is already created and the data modeling tool is creating the database scripts and will apply the scripts to create a database and/or tables in the database. Reverse Engineering captures information from a database or script file to create a physical data model. Creating Logical and Physical data model involves using the GUI to create a data model that conforms to business requirements. Splitting models of older version into separate logical and physical data models can create more manageable data models. 80. Answer: B. Reverse Engineering. Reverse Engineering creates a data model from an existing database. Forward Engineering the data model is already created and the data modeling tool is creating the database scripts and will apply the scripts to create a database and/or tables in the database. Creating Logical and Physical data model involves using the GUI to create a data model that conforms to business requirements. Importing a prior version data model is looking at an older version of the data model. 81. Answer: D. Create the Enterprise Data Model. Reverse Engineering benefits are: maintaining database, changing database types, analyzing the differences in databases. Reverse Engineering functionality cannot create the Enterprise Data Model unless the database has enterprise wide data. 82. Answer: D. Improving. Forward Engineering, Reverse Engineering and Comparisons are all typical Data Model tool functions. Improving is a function that does not exist in data modeling tools. Forward Engineering the data model is already created and the data modeling tool is creating the database scripts and will apply the scripts to create a database and/or tables in the database. Forward Engineering is also referred to as exporting. Reverse Engineering creates a data model from an existing database. The comparison function allows for comparisons between the data model and database; select the objects to compare and selectively import or export changes. 83. Answer: A. Keep data model and database synchronized. The benefit of Comparison function in data modeling tools is that it allows the data modeler or database to keep the data model and database synchronized. The comparison function allows for comparisons between the data model and database; select the objects to compare and selectively import or export changes. These are features of the comparison function. The main benefit is the data model and database is synchronized.

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84. Answer: A. Forward Engineering. Forward Engineering the data model is already created and the data modeling tool is creating the database scripts and will apply the scripts to create a database and/or tables in the database. Forward Engineering is also referred to as exporting. 85. Answer: C. Versioning. Versioning records who made the change and when the change was made to provide full project audit trail thus enabling rollback options when comparing versions. Change control is a function that allows knowing the impact of change before saving. Model Merge is simply merging two data models together. Submodeling breaks a data model into smaller models (e.g. by subject area) for ease of use. 86. Answer: B. Model Merge. Model Merge s merging of data models in an enterprise. Versioning records who made the change and when the change was made to provide full project audit trail thus enabling rollback options when comparing versions. Change control is a function that allows knowing the impact of change before saving. Submodeling breaks a data model into smaller models (e.g. by subject area) for ease of use. 87. Answer: D. Comparing two data models to detect changes. Model Merge features can be used to bring two data models together. Model Merge can: create an enterprise data model, bring individual data models together in a group, and merge two previously unrelated projects. If a data modeler wanted to compare two data models to detect changes, they would use a Comparison function if they were previously linked. 88. Answer: D. The functionality is not allowed. When importing and merging data models, data modelers can typically: create a new data model or update one of the existing data models. 89. Answer: D. Business Process Model. Although some tools offer linkages between data model and business process tools and have synchronization options, the tools are still typically separate. The benefit of this synchronization is it verifies the data models the business processes and vice versa. Data Modeling tools have the breadth of data models that represent: Enterprise Data Model; Logical Data Model; and Physical Data Model. 90. Answer: C. Synchronize changes between data models. The benefit of linkages and mapping between enterprise, logical and physical data models is to synchronize changes between data models. The data models do not need to be stored in the same file, but need to maintain links between the different data models. Different data models are used as they serve different purposes in the application development process. A transformation function may include items like many-to-many where the relationship is dissolved by an identifying entity.

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4.0 Data / Metadata Infrastructure Management Overview Establishing data modeling standards provides a framework and guidance for new projects being implemented and provides a strong foundation for changes applications and the data. Standards, like entity naming, increase data sharing opportunities, reduce data redundancy, and improve application interoperability. Data security and privacy is an area of growing legal regulation and enforcement. Data security can be derived from the three principles of: ability, Authorization, and Availability. ability is the concept that every must be responsible for their actions, so that in the event of any questionable activity or breach of policy, a specific can be identified. Authorization is a concept that access to data and system resources should be limited to a need to know basis, and that specific s must be specifically allowed such access. Availability is the concept that system and data resources must be accessible whenever they are needed. Data privacy is being classified by the content, critically to business and availability requirements. OECD Guidelines define the Protection of Privacy and Transborder Flow of Data and provide privacy protection in relation to personal data.

Topics Standards, Policies, Procedures, Guidelines Standards Management Process Data Models Data Elements Data Security and Privacy Data Security Principles Data Security Policy Types

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Questions 4.1 Standards, Policies, Procedures, Guidelines 1. What is the name of an entity? A. Customer B. Customers C. Customer_Name D. Name 2. Which is the best name for an entity describing employee information: A. EmployeeTable B. Employee_Table C. EmployeeTbl D. Employee 3. Which attributes follows best practises in naming? A. Social-insurance-number B. Social-insurance C. Social-insurance-code D. Social-insurance-numbers 4. When naming primary keys attributes follows best practises in naming? A. Customer-Ident B. Customer-Ids C. Customer-Id D. Customer-Identifier 5. Which of the following should be used when naming a relationship between an employee and a manager? A. Supervises B. Supervise C. Supervisor D. Is a 6. Which of the relationships capture a hierarchal relationship between employees: A. Has a B. Supervises C. Is a D. Manages

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7. Which of the following is the best description of a business rule: A. Repeat customers are categorized as type 01. B. Repeat customers are good for business. C. Repeat customers do not need a credit check. D. Repeat customers do not need a credit check if they are in good standing. 8. Data Integrity rules in a data warehouse environment include the following except: A. B. C. D.

Cleaning Redundancy resolution Business rule enforcement Random sampling.

9. What is the difference between ANSI and ISO/IEC? A. ISO/IEC form the specialized system for worldwide standardization, while ANSI that isters and coordinates the U.S. voluntary standardization and conformity assessment system. B. ISO/IEC cover the field of information technology while ANSI is concerned with certification. C. ISO/IEC cover the field of information technology while ANSI is concerned with Product Standards 10. After the creation of a Standards Management Process, which of the following is not considered: A. Approval B. Censure C. Enforcement D. Maintenance 11. Which of the following is not a notations typically used in logical and physical database design: A. Information Engineering B. IDEF1x C. Dimensional Modeling D. Conceptual Modeling

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12. Which one of the following is not a data element representation types: A. Amount B. Code C. Date D. Customer 13. A data element name that conforms to the ISO/IEC 11179 metadata registry naming convention does not have one of the following: A. Object B. Property C. Representation term D. Process Definition 14. In the ISO/IEC 11179 metadata is defined as: A. Data about data B. Data that defines and describes other data C. DNA of the data D. All information that is not the data itself. 15. What is the process for creating metadata when using a metadata registry? A. Attributing, Classifying, Defining, and ing B. Attributing, Classifying, Defining, and Maintaining C. Creation, Approval, Enforcement, ing D. Creation, Approval, Enforcement, Maintenance 16. Which of the following is not considered a sampling technique of data element audits: A. Random Sampling B. Systematic Sampling C. Cluster Sampling D. Standard Deviation. 17. In a data element audit, the data element must be: A. Valid and accurate B. Conformance of data values to its domain and business rules C. Complete D. Not null

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18. In a Data Warehouse, legacy element linkages are referred to as: A. Source System Metadata B. Data Staging Metadata C. DBMS Metadata D. Front Room Metadata 19. What should be standard for knowledge workers that analyze data in a data warehouse? A. Use data warehouse record and metadata to navigate back to the record in the source system. B. Complete the data in a data warehouse by adding postal information. C. Ability to consolidate the data in the data warehouse. D. Conduct a baseline assessment of the quality of the data. 20. Which of the following definition uses the best example of metadata principles? A. Employee ID Number - Number assigned to an employee. Employee - Person corresponding to the employee ID number. B. Employee – For the purpose of the data dictionary, employee is an internal stakeholder. C. Employee - an individual who has entered into or works under (or, where the employment has ceased, worked under) a contract of employment.

D. Employee - Someone who is hired by HR under the DMSP to provide services to a company.

4.2 Data Security and Privacy 21. The need for data security can be derived from three principles, which of the following does not apply? A. Agreement B. ability C. Authorization D. Availability 22. Logging into a system by supplying a name and is known as which data security principle? A. ability B. Authorization C. Availability D. Not a valid data security principle

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23. Authorization may be defined as: A. Authorization is a concept that access to data and system resources should be limited to a need to know basis, and that specific s must be specifically allowed such access. B. Authorization is the ability to grant power to make decisions from the data. C. Authorization is the policy that gives official instructions on the use of the data. D. Authorization is a digital document that describes a written permission from the issuer to use a service or a resource that the issuer controls or has access to use. The permission can be delegated. 24. Which data security principle defines that the system and data resources must be accessible whenever they are needed? A. ability B. Authorization C. Availability D. Not a valid data security principle 25. What is the main objective of the data steward responsibilities with respect to defining security policy? A. Define the security requirements, controls and mechanisms applicable to all data assets. B. Management of the data asset accessibility. C. Ensure compliance to the security policy. D. Define the data quality standards. 26. When defining the security policy, what is the trustee responsibility? A. Define the security requirements, controls and mechanisms applicable to all data assets. B. Management of the data asset accessibility. C. Ensure compliance to the security policy. D. Define the data quality standards. 27. What does the OECD Guidelines on the Protection of Privacy and Transborder Flows of Data protect? A. Provides privacy protection in relation to personal data. B. Determines how the data is transmitted between countries. C. Determines which countries can share data. D. Outlines the handling of data. 28. Which one of the following is not a typical Government security classified level? A. Top Secret / Secret Page 93 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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B. Confidential C. Restricted D. Classified 29. Which of the following applies the most when classifying data in a data warehouse? A. Only the summarized financial information needs to be confidential. B. Only detailed financial records need to be confidential. C. Data classification depends on the data content. D. All data in a data warehouse should be available. 30. Audit trail inspection is a classified under what type of security monitoring? A. Proactive B. Reactive C. Offensive D. Defensive 31. When data is considered mission critical, what is the class and data availability required, according to the Storage Networking Industry Association (SNIA) based upon Five 9s? A. Class 2 - 99% data availability B. Class 3 - 99.9% data availability C. Class 4 - 99.99% data availability D. Class 5 - 99.999% data availability 32. When data is classified as Class 1 90% data availability, what is the business classification of the data according to the Storage Networking Industry Association? A. Not important to Operations B. Important for Productivity C. Business Important Information D. Business Vital Information 33. Which of the following statements is the not true when data content is controlled in an organization? A. Business policies or regulatory rules require some/all data be retained “X” period of time. B. May need to prove records stored are “trustworthy” at later date C. High data value to an organization need to be accessible, available and protected D. Data availability is Class 1 – 90% data availability.

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34. Which of the following statements is the not true when data content is non-controlled in an organization? A. No business rules or regulations are requiring this data be kept for “X” period of time. B. Business just needs to keep the data archived and accessible C. High data value to an organization needs to be accessible, available and protected. D. Med-Low data value to an organization needs to be accessible and available.

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Quick Answers 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

A D A D A C C D A B D D D B A D A A A C A A A

24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34.

C B A A D C D D A D C

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Detailed Answers 1. Answer: A. Customer. Naming an entity should follow a standard and uniform approach. Entity Names should be simple, clear, and expressed in business . Entities should be in noun or adjective noun format; singular; in business ; and not process specific. Customers are plural. Customer_Name is not in business . Name is not clear to which name it is referring. 2. Answer: D. Employee. The best name for an entity describing employee information is simply Employee. Incorrect names include: EmployeeTable, Employee_Table, and EmployeeTbl. Employee_Table is not in business . EmployeeTbl is not clear. EmployeeTable is not in business . 3. Answer: A. Social-insurance-number. An attribute should be in the singular, consistent, and clearly defined in business . Properly defined attributes should define the data domain type like date, time, amount, code, name, quantity and description. Social-insure is unclear domain type. Social-insurance-numbers is plural. Social-insurance-code does not define the contents. 4. Answer: D. Customer-Identifier. An attribute should be in the singular, consistent, and clearly defined in business . Completely spelling out the attribute is preferred. If using an abbreviation, they should be as clear as possible and used consistently across the enterprise. If using abbreviations, whenever possible, use industry standard abbreviations. 5. Answer: A. Supervises. Relationships capture how two or more entities are related to one another. Relationships should be named after plural verbs. 6. Answer: C. Is a. Is a captures a hierarchal relationship between employees and the different classes of employees or subtypes. In this relationship, the new class or object has data or behavior aspects that are not part of the inherited class. 7. Answer: C. Repeat customers do not need a credit check. Business rules describe the operations, definitions and constraints for governing policies. The business rule needs to define in clear and concise business and easily applied. 8. Answer: D. Random sampling. Data integrity rules in a data warehouse environment include: cleaning, redundancy resolution and business rule enforcement. Random sampling is a technique and tool that may be used to conduct checks on the data. 9. Answer: A. ISO/IEC forms the specialized system for worldwide standardization, while ANSI that isters and coordinates the U.S. voluntary standardization and conformity assessment system. ISO (the International Organization for Standardization) and IEC (the International Electro technical Commission) form worldwide standards through technical committees. ANSI (The Page 97 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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American National Standards Institute), is the US representative to the ISO/IEC. 10. Answer: B. Censure. After the creation of a Standards Management Process, censure is not a considered activity. The four activities associated with the Standards Management Process are: Creation, Approval, Enforcement, and Maintenance. 11. Answer: D. Conceptual Modeling. The notations typically used in logical and physical database design are: information engineering, IDEF1x (ICAM DEFinition Language) and dimensional modeling. Conceptual Modeling is a high-level data model that leads to a logical model. 12. Answer: D. Customer. The following is not a data element type: Customer. Data element representation types are items like: Amount, Code, Date, Identifier, Name, Number, Text, Rate, and Year. Data elements are the fundamental units of data an Enterprise manages. 13. Answer: D. Process Definition. A data element name that conforms to the ISO/IEC 11179 metadata registry naming convention does not have of the following: Process Definition. It does have an object, property and representation term (Date, Time, etc) 14. Answer: B. Data that defines and describes other data. In the ISO/IEC 11179 metadata is defined as data that defines and describes other data. It specifies the kind and quality of metadata necessary to describe data, and it specifies the management and istration of that metadata in a metadata registry. 15. Answer: A. Attributing, Classifying, Defining, and ing. The processes for creating metadata when using a metadata registry are: Attributing, Classifying, Defining, and ing. 16. Answer D. Standard Deviation. The following is not considered a sampling technique of data element audits: Standard Deviation. Random, Systematic, and Cluster are all sampling techniques. Random chooses any record in the database. Systematic chooses every nth record. Cluster takes a subgroup of data based on a classification method like city. Standard deviation would be used to model the sample of data and calculate confidence that the data represents the whole. 17. Answer: A. Valid and accurate. In a data element audit, the data element must be valid and accurate. A data element could be complete (not null) and conform to its data values in its domain and business rules but still be inaccurate. 18. Answer: A. Source System Metadata. In a Data Warehouse, legacy element linkages are referred to as: Source System Metadata. DBMS Metadata is descriptions about the logical or physical data model. Data Staging Metadata is metadata around the staging area. Front Room Metadata is metadata for the front line s.

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19. Answer: A. Use data warehouse record and metadata to navigate back to the record in the source system. Prior to the source data being transformed to the data warehouse, it should be standardized, cleansed, completed, enhanced, consolidated and summarized where needed. The knowledge workers should be able to analyze the degree to which the data agrees with original source. Complete the data in a data warehouse by adding postal information and the ability to consolidate the data should be done prior to loading the data into the data warehouse. The knowledge worker should be told of the data quality in the data warehouse. In analyzing the data, they should not have to conduct a baseline assessment of the quality of the data. 20. Answer: C. Employee - an individual who has entered into or works under (or, where the employment has ceased, worked under) a contract of employment. Metadata principles are: state the essential meaning of the concept; be precise and unambiguous (Answer B); contain only commonly understood abbreviations(Answer C); be concise; be able to stand alone; be expressed without embedding rationale, functional usage, domain information, or procedural information; avoid circular reasoning (Answer A); and use the same terminology and consistent logical structure for related definitions. 21. Answer: A. Agreement. The need for data security can be derived from the three principles of: ability, Authorization, and Availability. 22. Answer: A. ability. ability is the concept that every must be responsible for their actions, so that in the event of any questionable activity or breach of policy, a specific can be identified. The specific security services that ability are identification, authentication, and auditing. Identification refers to a security service that recognizes a claim of identity by comparing a id offered with stored security information. Authentication refers to a security service that verifies the claimed identity of the , for example a . Auditability refers to a security service that records information of potential security significance. Authorization is a concept that access to data and system resources should be limited to a need to know basis, and that specific s must be specifically allowed such access. Availability is the concept that system and data resources must be accessible whenever they are needed. 23. Answer: A. Authorization is a concept that access to data and system resources should be limited to a need to know basis, and that specific s must be specifically allowed such access. Access control refers to a security service that allows or denies a request based on privilege, group information, or context. The specific security services that authorization are access control and confidentiality. Confidentiality refers to a security service that prevents disclosure of information to unauthorized parties while the information is in use or in transit, or while the information is being stored or destroyed. ability is the concept that every must be responsible for their actions, so that in the event of any questionable activity or breach of policy, a specific can be identified. Availability is the concept that system and data resources must be accessible whenever they are needed.

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24. Answer: C. Availability. Availability is the concept that system and data resources must be accessible whenever they are needed. The necessity for availability is dependent upon your particular business proposition. The specific security service that s availability is integrity. Integrity refers to a security service that guarantees data has not been altered, deleted, repeated, or rearranged during transmission, storage, processing, or recovery. ability is the concept that every must be responsible for their actions, so that in the event of any questionable activity or breach of policy, a specific can be identified. Authorization is a concept that access to data and system resources should be limited to a need to know basis, and that specific s must be specifically allowed such access. 25. Answer: B. Management of the data asset accessibility. The main objective of the data steward responsibilities with respect to defining the security policy is the management of the data asset accessibility. The data steward does not ensure compliance to the security policy, define the data quality standards, or define the security requirements, controls and mechanisms applicable to all data assets. 26. Answer: A. Define the security requirements, controls and mechanisms applicable to all data assets. The trustee is entrusted with the istration of the data assets. 27. Answer: A. Provides privacy protection in relation to personal data. OECD Guidelines on the Protection of Privacy and Transborder Flow of Data provides privacy protection in relation to personal data. The Guidelines apply to personal data, no matter if the company is public or private sectors, because of the potential detriment to civil liberties. The guidelines specify how to collect, store, process or disseminate personal information. 28. Answer: D. Classified. The typical security classified levels are top secret, secret, confidential, restricted and the lowest level of unclassified. The levels determine the impact on national security if the data was to be made public. Corporations typically have a similar type of classification structure. 29. Answer: C. Data classification depends on the data content. When classifying data in a data warehouse, the data content needs to be evaluated. In some cases the detailed data should be confidential and in others, the summarized data. The challenge in data privacy is to share data while protecting the identifiable information. 30. Answer: D. Defensive. Audit trail inspection is classified under a Defensive type of security monitoring. Other types of defensive monitoring are: Role Based Access Security and Process Rights Management. Offensive monitoring includes: Provisioning and Federated Identity Management. 31. Answer: D. Class 5 – 99.999% data availability. Mission critical data availability according to the Storage Networking Industry Association based upon Five 9s is Class 5 99.999%. The five classes are: Class 1 - 90% data availability: Not important to Operations Page 100 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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Class 2 - 99% data availability: Important for Productivity Class 3 - 99.9% data availability: Business Important Information Class 4 - 99.99% data availability: Business Vital Information Class 5 - 99.999% data availability: Mission Critical Information 32. Answer: A. Not important to Operations. When data is classified as Class 1 – 90% data availability, the business classification of the data according to the Storage Networking Industry Association is Not Important to Operations. The five classes are: Class 1 - 90% data availability: Not important to Operations Class 2 - 99% data availability: Important for Productivity Class 3 - 99.9% data availability: Business Important Information Class 4 - 99.99% data availability: Business Vital Information Class 5 - 99.999% data availability: Mission Critical Information 33. Answer: D. Data availability is Class 1 – 90% data availability. When data content is controlled in an organization, the organization has a compliance or fiduciary responsibility for the data. Therefore, the controlled data has a high data value to an organization and needs to be accessible, available and protected in a “trustworthy” state and retained for a defined period of time. Data Value defines the business significance of different classes of data and the degree to which data has to be accessible, available, and protected. 34. Answer: C. High data value to an organization needs to be accessible, available and protected. When data content is non-controlled in an organization, the organization does not have a compliance or fiduciary responsibility for the data, only a business usage. Therefore, the controlled data has med-low data value to an organization and needs to be archived and accessible and the retention period is defined by the business and useful of the data, not an external entity.

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5.0 Information Quality Management Overview Information quality is as important as the data in an organization, but often takes a back seat. If the data cannot be depended upon, used and leveraged within the organization it holds no value, ergo “Garbage In, Garbage Out”. Information Quality Principles set the foundation for evaluating the quality of data through: Information Quality Characteristics, Data Definition (or Information Product Specification) and Quality Characteristics. Once the “parameters” of the data have been define, information quality assessment and ongoing information audits should occur. Audit and assessments measure the quality of data, either in physical form (file, database, spreadsheet) or output from a process. Accessing the data may take the form of Random sampling, Cluster sampling or Systematic sampling. All results would be written in an Information Quality Report for either further investigation or action. Once the Information Quality improvements have been identified, and their root cause determined, the process that produces the defective data must be fixed, plus the physical data. A cycle of continuous improvement of the data quality needs to be implemented in every organization. One approach, the Shewhart cycle, named for Walter Shewhart, discussed the concept in his 1939 book, "Statistical Method From the Viewpoint of Quality Control", is the continuous improvement cycle of Plan, Do, Check, Act. Overall, organizations who want to maximize data and data management by exposing, mitigating and managing quality within their business will reap the rewards to prevent first and restore second.

Topics Information Quality Principles Definition Information Quality Characteristics Data Definition (or Information Product Specification) Quality Characteristics Information Quality Assessment / Audit Quality Assessment Characteristics Quality /Cost Measurement Information Quality Improvement Data Corrective Maintenance Data Movement Control Information Quality Process Improvement Information Quality Culture Transformation

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Questions 5.1 Information Quality Principles 1. What is the difference between data and information? A. Data is the representation of facts, information is data in context. B. Data is the context and Information is the content. C. Data is the value and Information is a valuable enterprise resource. D. Data is meaningful and information has context. 2. What is Information Quality? A. Quality data that enables knowledge workers to answer their questions. B. Correctness or accuracy of the data and the degree of usefulness and value data has to the organization. C. Quality data values in a data attribute field. D. Valid and meaningful information that the enterprise can make decisions upon. 3. Who benefits the most from Information Quality? A. Knowledge Workers B. Management C. Data Modeler D. Data Base 4. What type of data is required when the consequences of nonquality cause major process failure? A. Complete Quality data B. Accurate data C. Scientific data D. Zero-defect data 5. According to Larry English, what are the components that make up information? A. Information = Data + Definition B. Information = Data + Definition + Quality C. Information = Data + Definition + Presentation D. Information = Data + Definition + Content 6. What is Data Definition Quality? A. The definition, quality, and accuracy that govern data. B. The definition, domain value set, and business rules that govern data. C. The information about the data that meets manager expectations. D. The information that departmental knowledge workers depend on.

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7. What is needed to produce consistent high-quality information? A. Information Product Specification B. Data Definitions C. Customer Requirements D. Data Models 8. What are the features of Data Definition Quality? A. Data standards quality, data definition quality and information architecture quality. B. Data standards quality, data model quality and information architecture quality. C. Data models quality, domain type consistency, and definition quality. D. Business rule quality, data definition quality, and domain value quality. 9. What is Data Name quality? A. The name of the data plainly conveys the meaning of the objects named. B. The name of the data enables knowledge workers to easily define data completely and consistently across the organization. C. The name of the data that the enterprise needs to know about is consistent with the entity name. D. The name of the data indicates the type of data represented. 10. What is “Definition Conformance”? A. The measure of conformance of data its domain. B. The measure of conformance of data to the information product specification. C. To be consistent with the right level of granularity of the data values. D. To be consistent with the meaning of actual data values with its data definition. 11. Which of the following describes “the right level of granularity in the data values”? A. Definition Conformance B. Completeness C. Accuracy D. Precision 12. Which of the following describes “the characteristic of having all required values for the data fields”? A. Definition Conformance B. Completeness C. Accuracy D. Precision

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13. Which of the following depends on a reliable and precise recording of information? A. Definition Conformance B. Completeness C. Accuracy D. Precision 14. Which one of the following does nonduplication principles lead to the least? A. Identical values in multiple records. B. Duplicate records of a single event. C. Same data maintained in many independent, distributed databases. D. Its not a problem if there are standards and controls. 15. Which of the following describe when data is semantically equivalent? A. Consistency of Redundant data B. Timeliness C. Usefulness D. Objectivity 16. What does Information Float describe? A. The timetable to gather data. B. Length of time from when data is known, until it is available for a specific process or use. C. The average time required for data to be disseminated in the organization. D. The degree of variance in the data. 17. In a decision system, which of the following is considered most useful? A. Tabular data B. Data list C. Graphic presentation (bar chart, etc) D. It depends on the type of data 18. What is presentation clarity? A. The degree the knowledge worker can understand the meaning of the data through the presentation. B. Statements of fact with a neutral point of view including reporting without bias, and emphasis on initutive presentation to the knowledge worker. C. The ability to see the legends, rows and columns in a bar chart. D. The redesign producing high quality data content or presentation.

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19. Which one is a statement of fact with a neutral point of view, reporting without bias, and emphasis on initutive presentation to the knowledge worker called? A. Consistency of Redundant data B. Timeliness C. Usefulness D. Objectivity 20. What are the primary inherent information quality characteristics? A. Information Product Specification, Data Definitions, Data Models. B. Definition, domain value set, and business rules that govern data. C. Definition conformance, completeness, validity, accuracy, precision, nonduplication, and consistency of redundant data. D. Accessibility, timeliness, contextual clarity, derivation integrity, usability, completeness. 21. What are the primary pragmatic information quality characteristics? A. Information Product Specification, Data Definitions, Data Models. B. Definition, domain value set, and business rules that govern data. C. Definition conformance, completeness, validity, accuracy, precision, nonduplication, and consistency of redundant data. D. Accessibility, timeliness, contextual clarity, derivation integrity, usability, completeness. 22. Which definition for Employee-start-date conforms best to the Attribute definition quality principles? A. The date an employee was hired. B. The date a new employee was hired. C. The date that an employee first started with the company regardless of location. D. Tells when the employee first came to work 23. Using data name and definition consistency principles, which attributes best describes: The date a service is started with the customer? A. Product-start-date B. Service-start-date C. Service-release-date D. Data-Service-release-date

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24. Which of the following is the most appropriate to demonstrate Entity Type Name Clarity? A. Retail Customer B. Customers C. Buying Customers D. RTL_CUST 25. Which of the following is the most appropriate to demonstrate Attribute Name Clarity? A. Retail Customer Name B. Customers Identities C. Buying Customers Names D. RTL_CUST_NAME 26. Which one of the following is not known as a domain type? A. Date B. Time C. Amount D. Customer 27. For a cross-reference attribute, what is the most appropriate abbreviation following acronym clarity principles? A. Xref B. Cross-ref C. X-reference D. Crss-rfrnc 28. Why should names be appropriate to the knowledge workers? A. Attributes names are consistent where facts context are equivalent. B. Attributes names are equivalent to ease dissemination of information. C. Attribute names are accurate to express the meaning of the fact being defined. D. Attribute names are clear and concise business . 29. What role in an organization is responsible for the enterprise wide glossary? A. Business Steward B. Knowledge Worker C. Data Modeler D. Database

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30. What role in an organization is responsible for the business term definition? A. Knowledge Worker B. Data Modeler C. Database D. Subject Matter Expert 31. Where should business be defined? A. Glossary B. Data Model C. Data Dictionary D. Database 32. Which of the following is not true for Business rules: A. Based on technical or existing system limitations. B. Expressed in business . C. Complete and specific. D. Defines an aspect of business to take action.

5.2 Information Quality Assessment / Audit 33. Why is an information quality assessment/audit conducted? A. To measure the quality of data and information of processes. B. To measure the quality of data, either in physical form(file, database, spreadsheet) or output from a process. C. To measure the quality of data and information for statistical quality control. D. To assess the stable processes. 34. Which of the following is not a purpose of information quality assessment? A. Check the inputs and outputs of processes to ensure accuracy. B. Declare the accuracy and reliability of data. C. Provide to the technical people who create and maintain the data. D. Determine which processes should be retired. 35. Which data sampling technique is not valid when conducting an Information Quality Assessment? A. Linear Regression B. Random sampling C. Cluster sampling D. Systematic sampling

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36. Which of the following is not a data assessment test? A. Validity of business rule conformance. B. Timeliness and Nonduplication. C. Accuracy to surrogate source including derivation integrity. D. Usability 37. Which of the following is not a technique for Information Quality Report? A. Pareto Diagram. B. Bar Chart. C. Statistical Control Chart. D. Business Glossary List. 38. Which one of the following is not one of the redundant costs? A. Costs of inconsistent data. B. Cost to capture of interface. C. Data residing in multiple databases. D. Data residing in a single sharable database. 39. Which of the following do not determine the cost of data? A. Cost basis of developing and maintaining infrastructure. B. Cost to produce a product. C. Value basis uses the information to add value for the enterprise. D. Cost to define information requirements and design and build applications and databases. 40. Which of the following is not a direct result of process failure information costs: A. Duplicate catalogue mailings to a single customer. B. Knowledge workers re-ing the data. C. Incorrect purchasing decision made. D. Unhappy supplier trying to make corrections several times through customer . 41. What is the relationship between quality information and the business? A. Quality information costs the enterprise overall. B. Information enables the enterprise to accomplish its mission and goals. C. No relation between information and the bottom line. D. The relation between information and the bottom line cannot be quantified.

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5.3 Information Quality Improvement 42. Where is it best to correct defective data? A. In downstream databases. B. Fix the data at the source. C. Fix the process that produces the defective data by identifying root cause. D. Mark the defective data through metadata until it can be fixed. 43. What is the process that takes existing data, which is defective and brings the data to suitable levels of quality? A. Data Reengineering and cleansing process. B. Extract Transform and Load (ETL) process. C. Data Loading process. D. Metadata process. 44. Which one of the following will Data Architects not consider when embarking on an Information Product Improvement project? A. Source Data Cleansing B. Data Conversions C. Data Scrubbing D. Presenting data. 45. An analysis of the data revels that over 60% of the phone numbers in the database are the same number 000-0000. Further analysis reveals that the phone number cannot be null. A rule is added to the interface that the phone number cannot be zeros. This is known as: A. Source Data Cleansing B. Data Conversion C. Data Scrubbing D. Best Practice 46. In the source to target mapping of data, the data is standardized so that values depicting gender are the uniform. This is known as: A. Source Data Cleansing B. Data Conversion C. Data Scrubbing D. Best Practice

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47. What area is considered part of defining summary and derived data, adding data from external sources, consolidating data? A. Source Data Cleansing B. Data Conversion C. Data Scrubbing D. Best Practice 48. Which group should conduct the quality audit and control of data movement procedures? A. Internal Audit B. Knowledge Workers C. Data Conversion Specialists D. Information Steward 49. Which one of the following is not a cost of information quality? A. Nonquality information costs. B. Information quality assessments/audits. C. Information quality process improvements and prevention. D. Incorrect decisions made on poor quality information. 50. Which of the following is a proactive technique in Information Process Quality Improvement? A. Fix the symptoms. B. Ignore Problem signs until they become issues. C. Analyze root cause and eliminate the cause. D. Following Best Practise in IT System Management 51. Which one of the following techniques is not used in root cause analysis for determining quality issues? A. Cause-and-effect diagram B. Interrelationship diagram C. Current reality tree D. Value chain relationship diagram 52. Which step is not involved in the Shewhart cycle? A. Plan and Do B. Check C. Act D. Refine

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53. When implementing an Information Quality Culture Transformation which one of the following is most important to do well? A. Training B. Management Buy-in C. Define a Methodology D. Data Definition quality assessment process 54. Who is able for the integrity of the processes and quality of information? A. Knowledge Steward B. Managerial Information Steward C. Process Steward D. Business Information Steward 55. What is the first step an enterprise should take when embarking on an information quality program? A. Conduct an Information Quality Management Maturity Assessment and gap analysis. B. Create a vision, mission, and objectives for the information quality program. C. Appoint an Information Quality Leader. D. Conduct a customer satisfaction survey. 56. Which state is the least mature in the Information Quality Management Maturity Assessment? A. Uncertainty B. Awakening C. Enlightenment D. Wisdom and Certainty 57. Which state is characterized by knowing a data quality problem exists but not knowing what to do about it? A. Uncertainty B. Awakening C. Enlightenment D. Wisdom and Certainty 58. Which state has adopted a commitment to quality and implements the 14-point program? A. Uncertainty B. Awakening C. Enlightenment D. Wisdom and Certainty Page 112 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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59. Which state has prevention becoming a normal part of operations? A. Awakening B. Enlightenment C. Wisdom D. Certainty 60. Which state is the most mature in the Information Quality Management Maturity Assessment? A. Awakening B. Enlightenment C. Wisdom D. Certainty 61. When conducting an Information Quality Management Maturity Assessment, what are the five stages assessed across the Measurement Categories? A. Management understanding and attitude; Information Quality Organization Status; Information Quality Problem Handling; Cost of Information Quality as a Percent of Revenue or Operating Budget; Information Quality Improvement Actions; and Summary. B. Plan; Do; Check; Act C. Definition conformance, completeness, validity, accuracy, precision, nonduplication, and consistency of redundant data. D. Accessibility, timeliness, contextual clarity, derivation integrity, usability, completeness.

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Quick Answers 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

A B A D C B A A A D D B C C A B D A D C D C B

24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46.

A A D B A A D A A B D A D D D B B B C A D A B

47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61.

C D D C D D A B A A B C C D A

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Detailed Answers 1. Answer: A. Data is the representation of facts, information is data in context. Data is the raw material and information is the finished product. 2. Answer: B. Correctness or accuracy of the data and the degree of usefulness and value data has to the organization. Information Quality needs to have inherent quality (data accuracy) and pragmatic quality (usefulness and value to the enterprise process that enable accomplishing enterprise objectives). Data needs to the knowledge workers decision-making process or else it holds no value to the organization. 3. Answer: A. Knowledge Workers. Knowledge Workers benefit the most from Information Quality because they require data to do their jobs to the benefit of the endcustomer. 4. Answer: D. Zero-defect data. Zero-defect data is required when the consequences of nonquality cause major process failures. An example of data that must be accurate is domain reference data like: Medical diagnosis codes. 5. Answer: C. Information = Data + Definition + Presentation. As defined by Larry English, the three components that make up information are meaning (definition) of a fact (data) in a context (presentation). 6. Answer: B. The definition, domain value set, and business rules that govern data. Data Definition quality is the degree to which the data definition describes the meaning of the data and meets the needs of all stakeholders to understand the data and the context. 7. Answer: A. Information Product Specification. To produce consistent high-quality information an information product specification is needed. The specification states clearly and definitely the requirements along with acceptable product variations. 8. Answer: A. Data standards quality, data definition quality and information architecture quality. Data Definition Quality or “Information Product Specification Quality” is the specification for building well designed information architecture like manufacturing a Product. 9. Answer: A. The name of the data plainly conveys the meaning of the objects named. 10. Answer: D. To be consistent with the meaning of actual data values with its data definition. Definition conformance is comprised of data definition quality, validity and accuracy. 11. Answer: D. Precision. Precision is the characteristic of having the right level of granularity in the data values. For example, measurement of uptime of a system(99.999 or 99.5 ) needs to allow for a finer breakdown of values instead of 99% available.

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12. Answer: B. Completeness. Completeness is the characteristic of having all required values for the data fields. The completeness is measured by the extent of sparsity of the data. For example, Resident_Age should be a number greater than zero and not null. 13. Answer: C. Accuracy. Accuracy depends on a reliable and precise recording of information or agreement to original source. 14. Answer: C. Same data maintained in many independent, distributed databases. Nonduplication represents a one-to-one correlation between a record and the event. An example of duplication may reside in customer databases in a company where each department may capture their own customer information. 15. Answer: A. Consistency of Redundant data. When data is semantically equivalent System A Parent has domain values of (Father, Mother); System B Parent_No (1,2), System C Parent_Abbr (F, M) all mean the same thing. 16. Answer: B. Information Float is the length of time from when data is known, until it is available for a specific process or use. The measure of information float is the average time required for data to be disseminated in the organization. Both concepts are related to the timeliness: the relative availability of data within the time required by the knowledge worker. For example, an order may be filled out on an order form and faxed to the company. Timeliness represents the time taken to enter the order form into the system. 17. Answer: D. It depends on the type of data. Usefulness refers the form of information presentation and the degree to which a knowledge worker can readily interpret the results. 18. Answer: A. Presentation clarity is the degree the knowledge worker can understand the meaning of the data through the presentation. Presentation clarity avoids misinterpretation of the results. May also be referred to as contextual clarity. 19. Answer: D. Objectivity. Objectivity is a statement of fact with a neutral point of view, reporting without bias, and emphasis on intuitive presentation to the knowledge worker. Objectivity may also be referred to rightness or fact completeness. 20. Answer: C. Definition conformance, completeness, validity, accuracy, precision, nonduplication, and consistency of redundant data. 21. Answer: D. Accessibility, timeliness, contextual clarity, derivation integrity, usability, and completeness. 22. Answer: C. The date that an employee first started with the company regardless of location. The attribute quality definition describes in business the definition of the attribute that clearly defines one and only one attribute. 23. Answer: B. Service-start-date. Data name and definition consistency measures how well the name and definition is understood in the organization. Page 116 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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24. Answer: A. Retail Customer. Retail Customer is the most appropriate to demonstrate Entity Type Name Clarity. Entity type name clarity is easily understood by the knowledge worker and represents the objects. The characteristics of Entity Name Type Clarity are: singular nouns, business and easily comprehended by the knowledge worker. 25. Answer: A. Retail Customer Name. Retail Customer Name is the most appropriate to demonstrate Attribute Name Clarity. Attribute Name Clarity is easily understood by the knowledge worker and represents the facts. The characteristics of Attribute Name Clarity are: business , easily associated with the entity type and easily comprehended by the knowledge worker. 26. Answer: D. Customer. Domain type consistency (also known as class word) in an attribute represents the type of data stored. For example, Start-Date attribute has a domain type of date and the valid values would be a subset of all possible dates. Typical domain types include: date, time, amount, identifier (id), amount, code, name, quantity, percent, rate, and description. 27. Answer: B. Cross-ref. The most appropriate abbreviation for acronym clarity for the term cross-reference is Cross-ref. When using abbreviations, they should be documented in a single, enterprise-wide standards abbreviation list that is used consistently throughout the enterprise. Rules of thumb for creating abbreviations are: use industry-standards or universally accepted abbreviations where applicable, use short abbreviations without loss of meaning, and always use the first letter of the term. 28. Answer: A. Attributes names are consistent where facts context are equivalent. Names should be appropriate or consistent across the enterprise even across different formats of presentation and storage formats. 29. Answer: A. Business Steward. A Business Steward in the enterprise is responsible for keeping the enterprise wide glossary current in an organization. The responsibilities include adding, changing and deleting the definitions as needed. 30. Answer: D. Subject Matter Expert. A Subject Matter Expert or Business Information Steward in the enterprise is responsible for keeping the business term definitions current in an organization. The responsibilities include adding, changing and deleting the definitions as needed. 31. Answer: A. The business should be defined in the glossary that is enterprise wide. The glossary can take on several forms. 32. Answer: A. Based on technical or existing system limitations. Business rules should not be due to a technical or existing system limitation. Business rules should be expressed in business , defines an aspect of the business to take action, complete and specific, plus defines who, what, when, why and how and identities any exceptions. Page 117 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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33. Answer: B. To measure the quality of data, either in physical form (file, database, spreadsheet) or output from a process. The information quality assessment is conducted for the benefit of the knowledge workers. 34. Answer: D. Determine which processes should be retired. The purpose of information quality assessments is evaluating the processes and data, certify the data, and providing plus measuring against the baseline to calculate the costs of nonquality. 35. Answer: A. Linear Regression. Linear Regression is not a valid sampling technique when conducting an Information Quality assessment. 36. Answer: D. Usability. Usability is not a concern of the data assessment test. Data assessment tests measure: Validity of business rule conformance, Timeliness, Nondupliction, Accuracy to surrogate source including derivation integrity, and Consistency of data. 37. Answer: D. Business Glossary List. The Information Quality Report deduces and reports on the data assessment using Pareto Diagrams, Bar Chart, Statistical Control Charts and outputs from Information quality analysis software. 38. Answer: D. Data residing in a single sharable database. The cost of redundancy is part of the cost formula of information quality in the value basis component. Redundant costs occur when the data is contained in multiple databases. There is a cost to capture and control all the multiple databases and of the inconsistent or inaccurate data to the organization. Data residing in a database has only potential value. Information value occurs through usage only. 39. Answer: B. Cost to define the interface to acquire customer data. The cost of data is comprised of two areas: cost basis and value basis. Cost basis is the cost of developing and maintaining infrastructure. It is made up of the cost to define information requirements, develop information, application, and technology architectures; and to design and build applications and databases. Value basis uses the information to add value for the enterprise. 40. Answer: B. Knowledge workers re-ing the data. Process failure information costs result in spent costs, liability and exposure costs, and recovery costs. 41. Answer: B. Information enables the enterprise to accomplish its mission and goals. The enterprise business performance objectives need to be aligned to measuring information quality. 42. Answer: C. Fix the process that produces the defective data by identifying root cause. Data cleansing fixes the data. Information process quality improvements fix the process that produces the defective data. The process is typically iterative that involves the Page 118 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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cycles of: planning, implementing, assessing and rollout (Shewhart cycle of Plan-DoCheck-Act or PDCA). 43. Answer: A. Data Reengineering and cleansing process. The process that takes existing data, which is defective and brings the data to suitable levels of quality, is known as data reengineering and cleansing. Data reengineering is similar to reverse engineering but only looks at the data not the application or system. It mainly focuses on how the data is used in an organization and can work backwards to the data models. Data reengineering often results in a deeper understanding of data assets of the enterprise and may lead to areas like: data consolidation, data architecture, and data acquisition strategies. Data Cleansing is the act of identifying and correcting data. Correcting data involves cleaning up data that is incorrect, out-of-date, redundant, incomplete, or formatted incorrectly. 44. Answer: D. Presenting data. Data Architects will consider source data cleansing, data conversions, and data scrubbing when embarking on an Information Product Improvement project. These are known as the three data cleansing areas: Source Data Cleansing, Data Conversions and Data Scrubbing. 45. Answer: A. Source Data Cleansing. Source data cleansing improves existing data quality where the data is initially stored. 46. Answer: B. Data Conversion. Data conversion is the act of mapping the source to target and improving the quality of data by correcting, standardizing, de-duplicating, completing and formatting. 47. Answer: C. Data Scrubbing. Data scrubbing is an act of defining summary and derived data, adding data from external sources, consolidating data. 48. Answer: D. Information Steward. Information Steward should conduct quality audit and control of data movement procedures. They should get input from internal audit, knowledge workers and data conversion specialists. 49. Answer: D. Incorrect decisions made on poor quality information. There are three categories of information quality costs: Nonquality information costs (process, rework, lost and missed opportunity costs); Information quality assessments/audits; and Information quality process improvements and prevention. 50. Answer: C. Analyze root cause and eliminate the cause. Information Process Quality Improvement has two elemental processes: reactive and proactive. Proactive process involves conducting root cause analysis. Root Cause Analysis identifies not only what and how an event occurred, but also why it happened. Only when an analysis of why an event or failure occurred will corrective measures are found. 51. Answer: D: Value chain relationship diagram. Value chain relationship diagram is not a type of root cause analysis technique. Cause-and-effect diagram (Ishikawa or Page 119 of 122 Copyright © 2006 by DAMA International & DAMA International Foundation. All rights reserved.

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fishbone diagram) breaks down causes into detailed categories so they can be organized into related factors to identify root cause. Interrelationship diagram quantifies the relationships between factors and classifies causes leading to root cause. Current reality tree classifies interdependent relationships between effects leading to the determination of root cause. 52. Answer: D. Refine. The Shewhart cycle of Plan-Do-Check-Act or PDCA because known as the Deming cycle, is the foundation to improve information process quality. 53. Answer: A. Training. Training is most essential when implementing an Information Quality Culture Transformation for both management and staff. The training needs to cover why quality is fundamental to the enterprise and how to achieve quality. When defining training requirements, identify the role and their responsibilities toward information quality for example: general information, policies and processes, usage, and information management principles. Next define their training requirements and learning objectives for each role. 54. Answer: B. Managerial Information Steward. The Managerial Information Steward is able for the integrity of the processes and quality of information. Knowledge steward is able for the use of information. Process Steward is able for the definition of a business process. Business Information is able for validating the definition of data. 55. Answer: A. Conduct an Information Quality Management Maturity Assessment and gap analysis. When embarking on an information quality program, the first step an enterprise should take is to conduct an Information Quality Management Maturity Assessment and gap analysis to determine the current state of the organization and where they would like to be in the future. 56. Answer: A. Uncertainty. The Uncertainty state is Stage 1 and the least mature in the Information Quality Management Maturity Assessment. In the Uncertainty stage, Information quality is not considered a management tool. When issues occur they are dealt with in a reactive manner. 57. Answer: B. Awakening. The Awakening state is Stage 2 and is characterized by knowing a data quality problem exists but not knowing what to do about it in the Information Quality Management Maturity Assessment. In the Awakening stage, Information quality issues have been identified but management does not commit to their resolution. When issues occur they are cleaned up rather than fixed at the source and dealt with in a tight scope. 58. Answer: C. Enlightenment. The Enlightenment state is Stage 3 and is characterized by adopting a commitment to quality and implementing the 14-point program in the Information Quality Management Maturity Assessment. In the Enlightenment stage, Quality Improvement Program is implemented with communication and resolution.

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59. Answer: C. Wisdom. The Wisdom state is Stage 4 and is characterized by implemented a program of prevention in the Information Quality Management Maturity Assessment. In the Wisdom stage, Quality Improvement Program is mastered with quality being integrated into all areas and a routine part of enterprise. 60. Answer: D. Certainty. The Certainty state is Stage 5 and is the most mature in the Information Quality Management Maturity Assessment. In the Wisdom stage, Quality Improvement Program is an essential part of the enterprise. 61. Answer: A. Management understanding and attitude; Information Quality Organization Status; Information Quality Problem Handling; Cost of Information Quality as a Percent of Revenue or Operating Budget; Information Quality Improvement Actions; and Summary. To conduct an Information Quality Management Maturity Assessment, the five stages are assessed across the following Measurement Categories: Management understanding and attitude; Information Quality Organization Status(maturity of information quality in the enterprise); Information Quality Problem Handling (acts or reacts to issues); Cost of Information Quality as a Percent of Revenue or Operating Budget; Information Quality Improvement Actions; and Summary. Capability can be quantified using measurable criteria and is realized through an evolutionary not revolutionary process.

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Selected Bibliography Brackett, Michael, DATA SHARING: USING A COMMON DATA ARCHITECTURE, John Wiley, 1994, ISBN 04711309931. DAMA International & DAMA Chicago Standards Committee, “DATA MANAGEMENT ASSOCIATION: GUIDELINES TO IMPLEMENTING DATA RESOURCE MANAGEMENT, DAMA International, 4th edition, 2002. English, Larry P., IMPROVING DATA WAREHOUSE AND BUSINESS INFORMATION QUALITY, John Wiley & Sons, 1999, ISBN: 0471253839. Fowler, Martin, UML DISTILLED: APPLYING THE STANDARD OBJECT MODELING LANGUAGE, Addison-Wesley, 1997, ISBN: 0-201-32563-2. Inmon, W.H. BUILDING THE DATA WAREHOUSE, John Wiley, 2002, ISBN 0-471081302. Kimball, Ralph, THE DATA WAREHOUSE TOOLKIT, John Wiley, 1996, ISBN: 0471-15337-0. Marco, David, BUILDING AND MANAGING THE META DATA REPOSITORY: A FULL LIFECYCLE GUIDE, John Wiley, 2002, ISBN: 0471355232. McFadden, Fred R., Hoffer, Jeffrey A., and Prescott, Mary B. MODERN DATABASE MANAGEMENT, Fifth Edition, Addison-Wesley, 1999, ISBN 0-8053-6054-9. Simsion, Graeme and Witt, Graham, DATA MODELING ESSENTIALS, Third Edition, Morgan Kaufman, 2004, ISBN: 0126445516. Tannenbaum, Adrienne, METADATA SOLUTIONS: USING METAMODELS, REPOSITORIES, XML, AND ENTERPRISE PORTALS TO ACHIEVE INFORMATION ON DEMAND, Addison-Wesley, 2001, ISBN: 0201719762. Watson, Richard T. DATA MANAGEMENT: DATABASES AND ORGANIZATIONS, John Wiley & Sons, 2002, ISBN 0-471-41845-5.

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