Azure Databricks A Complete Guide - 2020 Edition 1n6342

Azure Databricks Complete Self-Assessment Guide

The guidance in this Self-Assessment is based on Azure Databricks best practices and standards in business process architecture, design and quality management. The guidance is also based on the professional judgment of the individual collaborators listed in the Acknowledgments.

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You are licensed to use the Self-Assessment contents in your presentations and materials for internal use and customers without asking us - we are here to help. All rights reserved for the book itself: this book may not be reproduced or transmitted in any form by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.

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Copyright © by The Art of Service http://theartofservice.com [email protected]

About The Art of Service

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us when you need any with this Self-Assessment and any

help with templates, blue-prints and examples of standard documents you might need:

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Included Resources - how to access

Included with your purchase of the book is the Azure Databricks SelfAssessment Spreadsheet Dashboard which contains all questions and SelfAssessment areas and auto-generates insights, graphs, and project RACI planning - all with examples to get you started right away.

How? Simply send an email to [email protected] with this books’ title in the subject to get the Azure Databricks Self Assessment Tool right away.

You will receive the following contents with New and Updated specific criteria: •The latest quick edition of the book in PDF •The latest complete edition of the book in PDF, which criteria correspond to the criteria in... •The Self-Assessment Excel Dashboard, and... •Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation •In-depth specific Checklists covering the topic •Project management checklists and templates to assist with implementation

INCLUDES LIFETIME SELF ASSESSMENT UPDATES

Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Get it now- you will be glad you did - do it now, before you forget.

Send an email to [email protected] with this books’ title in the subject to get the Azure Databricks Self Assessment Tool right away.

Purpose of this Self-Assessment

This Self-Assessment has been developed to improve understanding of the requirements and elements of Azure Databricks, based on best practices and standards in business process architecture, design and quality management.

It is designed to allow for a rapid Self-Assessment to determine how closely existing management practices and procedures correspond to the elements of the Self-Assessment.

The criteria of requirements and elements of Azure Databricks have been rephrased in the format of a Self-Assessment questionnaire, with a sevencriterion scoring system, as explained in this document.

In this format, even with limited background knowledge of Azure Databricks, a manager can quickly review existing operations to determine how they measure up to the standards. This in turn can serve as the starting point of a ‘gap analysis’ to identify management tools or system elements that might usefully be implemented in the organization to help improve overall performance.

How to use the Self-Assessment

On the following pages are a series of questions to identify to what extent your Azure Databricks initiative is complete in comparison to the requirements set in standards.

To facilitate answering the questions, there is a space in front of each question to enter a score on a scale of ‘1’ to ‘5’.

1 Strongly Disagree 2 Disagree 3 Neutral 4 Agree 5 Strongly Agree

Read the question and rate it with the following in front of mind:

‘In my belief, the answer to this question is clearly defined’.

There are two ways in which you can choose to interpret this statement; 1.how aware are you that the answer to the question is clearly defined 2.for more in-depth analysis you can choose to gather evidence and confirm the answer to the question. This obviously will take more time, most SelfAssessment s opt for the first way to interpret the question and dig deeper later on based on the outcome of the overall Self-Assessment.

A score of ‘1’ would mean that the answer is not clear at all, where a ‘5’ would mean the answer is crystal clear and defined. Leave emtpy when the question is not applicable or you don’t want to answer it, you can skip it without affecting your score. Write your score in the space provided.

After you have responded to all the appropriate statements in each section, compute your average score for that section, using the formula provided, and round to the nearest tenth. Then transfer to the corresponding spoke in the Azure Databricks Scorecard on the second next page of the Self-Assessment.

Your completed Azure Databricks Scorecard will give you a clear presentation of which Azure Databricks areas need attention.

Azure Databricks Scorecard Example

Example of how the finalized Scorecard can look like:

Azure Databricks Scorecard

Your Scores:

BEGINNING OF THE SELF-ASSESSMENT:

Table of Contents

About The Art of Service7

Included Resources - how to access7 Purpose of this Self-Assessment9 How to use the Self-Assessment10 Azure Databricks Scorecard Example12

Azure Databricks Scorecard13

BEGINNING OF THE SELF-ASSESSMENT:14 CRITERION #1: RECOGNIZE15

CRITERION #2: DEFINE:24

CRITERION #3: MEASURE:35

CRITERION #4: ANALYZE:51

CRITERION #5: IMPROVE:67

CRITERION #6: CONTROL:77

CRITERION #7: SUSTAIN:88 Azure Databricks and Managing Projects, Criteria for Project Managers:114 1.0 Initiating Process Group: Azure Databricks115

1.1 Project Charter: Azure Databricks117

1.2 Stakeholder : Azure Databricks119

1.3 Stakeholder Analysis Matrix: Azure Databricks120

2.0 Planning Process Group: Azure Databricks122

2.1 Project Management Plan: Azure Databricks124

2.2 Scope Management Plan: Azure Databricks126

2.3 Requirements Management Plan: Azure Databricks128

2.4 Requirements Documentation: Azure Databricks130

2.5 Requirements Traceability Matrix: Azure Databricks132

2.6 Project Scope Statement: Azure Databricks134

2.7 Assumption and Constraint Log: Azure Databricks136

2.8 Work Breakdown Structure: Azure Databricks138

2.9 WBS Dictionary: Azure Databricks140

2.10 Schedule Management Plan: Azure Databricks142

2.11 Activity List: Azure Databricks144

2.12 Activity Attributes: Azure Databricks146

2.13 Milestone List: Azure Databricks148

2.14 Network Diagram: Azure Databricks150

2.15 Activity Resource Requirements: Azure Databricks152

2.16 Resource Breakdown Structure: Azure Databricks154

2.17 Activity Duration Estimates: Azure Databricks156

2.18 Duration Estimating Worksheet: Azure Databricks158

2.19 Project Schedule: Azure Databricks160

2.20 Cost Management Plan: Azure Databricks162

2.21 Activity Cost Estimates: Azure Databricks164

2.22 Cost Estimating Worksheet: Azure Databricks166

2.23 Cost Baseline: Azure Databricks168

2.24 Quality Management Plan: Azure Databricks170

2.25 Quality Metrics: Azure Databricks172

2.26 Process Improvement Plan: Azure Databricks174

2.27 Responsibility Assignment Matrix: Azure Databricks176

2.28 Roles and Responsibilities: Azure Databricks178

2.29 Human Resource Management Plan: Azure Databricks180

2.30 Communications Management Plan: Azure Databricks182

2.31 Risk Management Plan: Azure Databricks184

2.32 Risk : Azure Databricks186

2.33 Probability and Impact Assessment: Azure Databricks188

2.34 Probability and Impact Matrix: Azure Databricks190

2.35 Risk Data Sheet: Azure Databricks192

2.36 Procurement Management Plan: Azure Databricks194

2.37 Source Selection Criteria: Azure Databricks196

2.38 Stakeholder Management Plan: Azure Databricks198

2.39 Change Management Plan: Azure Databricks200

3.0 Executing Process Group: Azure Databricks202

3.1 Team Member Status Report: Azure Databricks204

3.2 Change Request: Azure Databricks206

3.3 Change Log: Azure Databricks208

3.4 Decision Log: Azure Databricks210

3.5 Quality Audit: Azure Databricks212

3.6 Team Directory: Azure Databricks215

3.7 Team Operating Agreement: Azure Databricks217

3.8 Team Performance Assessment: Azure Databricks219

3.9 Team Member Performance Assessment: Azure Databricks221

3.10 Issue Log: Azure Databricks223

4.0 Monitoring and Controlling Process Group: Azure Databricks224

4.1 Project Performance Report: Azure Databricks226

4.2 Variance Analysis: Azure Databricks228

4.3 Earned Value Status: Azure Databricks230

4.4 Risk Audit: Azure Databricks232

4.5 Contractor Status Report: Azure Databricks234

4.6 Formal Acceptance: Azure Databricks236

5.0 Closing Process Group: Azure Databricks238

5.1 Procurement Audit: Azure Databricks240

5.2 Contract Close-Out: Azure Databricks242

5.3 Project or Phase Close-Out: Azure Databricks244

5.4 Lessons Learned: Azure Databricks246 Index248

CRITERION #1: RECOGNIZE

INTENT: Be aware of the need for change. Recognize that there is an unfavorable variation, problem or symptom.

In my belief, the answer to this question is clearly defined:

5 Strongly Agree

4 Agree

3 Neutral

2 Disagree

1 Strongly Disagree

1. What problems are ideal challenges for quantum computers? <--- Score

2. Is the range of stakeholder goals and values identified and reflected? <--- Score

3. Are Azure Databricks changes recognized early enough to be approved through the regular process? <--- Score

4. What is the nothing at stake problem? <--- Score

5. What are the minority interests and what amount of minority interests can be recognized? <--- Score

6. What measures are needed to protect privacy and ensure security? <--- Score

7. How does your organization go about generating the ideas needed to enrich customer experiences in a way that makes your brand memorable? <--- Score

8. What vendors make products that address the Azure Databricks needs?

<--- Score

9. How does it fit into your organizational needs and tasks? <--- Score

10. To what extent does management recognize Azure Databricks as a tool to increase the results? <--- Score

11. Can management personnel recognize the monetary benefit of Azure Databricks? <--- Score

12. As a sponsor, customer or management, how important is it to meet goals, objectives? <--- Score

13. Are controls defined to recognize and contain problems? <--- Score

14. How can you quickly identify an event and take an action to help the customer? <--- Score

15. What additional tools do you need? <--- Score

16. What are the key problems with Spark? <--- Score

17. What do you need to do to get there? <--- Score

18. To what extent does each concerned units management team recognize Azure Databricks as an effective investment? <--- Score

19. What does your customer need? <--- Score

20. What business or IT needs do you expect SPARK Ignition to fill for your team? <--- Score

21. Why do you need to manage the flow?

<--- Score

22. What is the clients problem or need? <--- Score

23. When a Azure Databricks manager recognizes a problem, what options are available? <--- Score

24. How do you recognize an Azure Databricks objection? <--- Score

25. What makes this problem worth solving? <--- Score

26. How do you stay flexible and focused to recognize larger Azure Databricks results? <--- Score

27. Would you recognize a threat from the inside? <--- Score

28. Do you recognize Azure Databricks achievements? <--- Score

29. How are you going to measure success? <--- Score

30. What are the primary transactions or events in the case? <--- Score

31. What problem are you trying to solve? <--- Score

32. What is the recognized need? <--- Score

33. What practices helps your organization to develop its capacity to recognize patterns? <--- Score

34. What are the stakeholder objectives to be achieved with Azure Databricks? <--- Score

35. Will a response program recognize when a crisis occurs and provide some level of response? <--- Score

36. Does Azure Databricks create potential expectations in other areas that need to be recognized and considered? <--- Score

37. What tools and technologies are needed for a custom Azure Databricks project? <--- Score

38. What problems are you facing and how do you consider Azure Databricks will circumvent those obstacles? <--- Score

39. What steps do you need to configure Hive Queries to run on Spark Engine? <--- Score

40. To what extent would your organization benefit from being recognized as a award recipient? <--- Score

41. Is the need for organizational change recognized? <--- Score

42. What are the expected benefits of Azure Databricks to the stakeholder? <--- Score

43. What issues and areas need to be considered? <--- Score

44. Which enterprise systems need to be connected? <--- Score

45. What problems does it solve? <--- Score

46. What kind of is needed? <--- Score

47. Are employees recognized for desired behaviors? <--- Score

48. Are losses recognized in a timely manner? <--- Score

49. How do you recognize an objection? <--- Score

50. What training and capacity building actions are needed to implement proposed reforms? <--- Score

51. What would happen if Azure Databricks weren’t done? <--- Score

52. Who else hopes to benefit from it? <--- Score

53. What situation(s) led to this Azure Databricks Self Assessment? <--- Score

54. Are there any specific expectations or concerns about the Azure Databricks team, Azure Databricks itself?

<--- Score

55. Which configuration settings should you modify to alleviate the performance issues? <--- Score

56. Are employees recognized or rewarded for performance that demonstrates the highest levels of integrity? <--- Score

57. How are the Azure Databricks’s objectives aligned to the group’s overall stakeholder strategy? <--- Score

58. Which compute target should you identify? <--- Score

59. Are there recognized Azure Databricks problems? <--- Score

60. How could the problem have been avoided? <--- Score

61. Are your offers relevant in the moment to a customers needs and expectations? <--- Score

62. Do you have/need 24-hour access to key personnel? <--- Score

63. Should you invest in industry-recognized qualifications? <--- Score

64. How will you recognize and celebrate results? <--- Score

65. Mass customization; what customers need a lot? <--- Score

66. What technologies and trends are emerging for particular industries with particular needs? <--- Score

67. Why would you need a custom activity?

<--- Score

68. What does Azure Databricks success mean to the stakeholders? <--- Score

69. How much are sponsors, customers, partners, stakeholders involved in Azure Databricks? In other words, what are the risks, if Azure Databricks does not deliver successfully? <--- Score

Add up total points for this section: _____ = Total points for this section

Divided by: ______ (number of statements answered) = ______ Average score for this section

Transfer your score to the Azure Databricks Index at the beginning of the SelfAssessment.

CRITERION #2: DEFINE:

INTENT: Formulate the stakeholder problem. Define the problem, needs and objectives.

In my belief, the answer to this question is clearly defined:

5 Strongly Agree

4 Agree

3 Neutral

2 Disagree

1 Strongly Disagree

1. How was the ‘as is’ process map developed, reviewed, verified and validated? <--- Score

2. Has/have the customer(s) been identified? <--- Score

3. Is scope creep really all bad news? <--- Score

4. Which service should you use to meet each requirement? <--- Score

5. Has the improvement team collected the ‘voice of the customer’ (obtained – qualitative and quantitative)? <--- Score

6. Is full participation by in regularly held team meetings guaranteed? <--- Score

7. Has everyone on the team, including the team leaders, been properly trained? <--- Score

8. How is the team tracking and documenting its work? <--- Score

9. Is the Azure Databricks scope manageable? <--- Score

10. Is Azure Databricks linked to key stakeholder goals and objectives? <--- Score

11. How did the Azure Databricks manager receive input to the development of a Azure Databricks improvement plan and the estimated completion dates/times of each activity? <--- Score

12. Are team charters developed? <--- Score

13. How will the Azure Databricks team and the group measure complete success of Azure Databricks? <--- Score

14. Is there a completed SIPOC representation, describing the Suppliers, Inputs, Process, Outputs, and Customers? <--- Score

15. Is there regularly 100% attendance at the team meetings? If not, have appointed substitutes attended to preserve cross-functionality and full representation? <--- Score

16. Has anyone else (internal or external to the group) attempted to solve this problem or a similar one before? If so, what knowledge can be leveraged from these previous efforts? <--- Score

17. What specifically is the problem? Where does it occur? When does it occur? What is its extent? <--- Score

18. If substitutes have been appointed, have they been briefed on the Azure Databricks goals and received regular communications as to the progress to date? <--- Score

19. What are (control) requirements for Azure Databricks Information? <--- Score

20. Is Azure Databricks currently on schedule according to the plan? <--- Score

21. What should you use to meet each requirement? <--- Score

22. Is the team adequately staffed with the desired cross-functionality? If not, what additional resources are available to the team? <--- Score

23. Is the team equipped with available and reliable resources? <--- Score

24. How many stages will this code require? <--- Score

25. What Azure Databricks requirements should be gathered? <--- Score

26. What baselines are required to be defined and managed? <--- Score

27. What is the definition of Azure Databricks excellence?

<--- Score

28. When is the estimated completion date? <--- Score

29. Do the problem and goal statements meet the SMART criteria (specific, measurable, attainable, relevant, and time-bound)? <--- Score

30. How does the Azure Databricks manager ensure against scope creep? <--- Score

31. Will team regularly document their Azure Databricks work? <--- Score

32. Is there a critical path to deliver Azure Databricks results? <--- Score

33. Has a team charter been developed and communicated? <--- Score

34. What sources do you use to gather information for a Azure Databricks study? <--- Score

35. How often are the team meetings? <--- Score

36. Are customers identified and high impact areas defined? <--- Score

37. What is out of scope? <--- Score

38. Have the customer needs been translated into specific, measurable requirements? How? <--- Score

39. What key stakeholder process output measure(s) does Azure Databricks leverage and how? <--- Score

40. Are customer(s) identified and segmented according to their different needs and requirements?

<--- Score

41. What customer methods were used to solicit their input? <--- Score

42. What are the dynamics of the communication plan? <--- Score

43. Is the current ‘as is’ process being followed? If not, what are the discrepancies? <--- Score

44. Will team perform Azure Databricks work when assigned and in a timely fashion? <--- Score

45. What are the rough order estimates on cost savings/opportunities that Azure Databricks brings? <--- Score

46. When is/was the Azure Databricks start date? <--- Score

47. How important is spark streaming to your use case? <--- Score

48. What are requirements for this to work? <--- Score

49. Are stakeholder processes mapped? <--- Score

50. Are different versions of process maps needed to for the different types of inputs? <--- Score

51. Is data collected and displayed to better understand customer(s) critical needs and requirements. <--- Score

52. Is a fully trained team formed, ed, and committed to work on the Azure Databricks improvements? <--- Score

53. Has a Azure Databricks requirement not been met? <--- Score

54. What would be the goal or target for a Azure Databricks’s improvement team? <--- Score

55. Who are the Azure Databricks improvement team , including Management Leads and Coaches? <--- Score

56. Is there a completed, verified, and validated high-level ‘as is’ (not ‘should be’ or ‘could be’) stakeholder process map? <--- Score

57. Are task requirements clearly defined? <--- Score

58. How do you keep key subject matter experts in the loop? <--- Score

59. How will variation in the actual durations of each activity be dealt with to ensure that the expected Azure Databricks results are met?

<--- Score

60. How do you gather requirements? <--- Score

61. Has a high-level ‘as is’ process map been completed, verified and validated? <--- Score

62. What is the scope of Azure Databricks? <--- Score

63. What are the compelling stakeholder reasons for embarking on Azure Databricks? <--- Score

64. Has a project plan, Gantt chart, or similar been developed/completed? <--- Score

65. Does the team have regular meetings? <--- Score

66. Is the team sponsored by a champion or stakeholder leader? <--- Score

67. Is there a Azure Databricks management charter, including stakeholder case, problem and goal statements, scope, milestones, roles and responsibilities, communication plan? <--- Score

68. Has the Azure Databricks work been fairly and/or equitably divided and delegated among team who are qualified and capable to perform the work? Has everyone contributed? <--- Score

69. Which service should you use for each requirement? <--- Score

70. Has the direction changed at all during the course of Azure Databricks? If so, when did it change and why? <--- Score

71. What constraints exist that might impact the team? <--- Score

72. Are there different segments of customers? <--- Score

73. When are meeting minutes sent out? Who is on the distribution list? <--- Score

74. Are improvement team fully trained on Azure Databricks? <--- Score

75. What are the tasks and definitions? <--- Score

76. What critical content must be communicated – who, what, when, where, and how? <--- Score

77. Is the team formed and are team leaders (Coaches and Management Leads) assigned? <--- Score

78. What are the Roles and Responsibilities for each team member and its leadership? Where is this documented?

<--- Score

79. Is the improvement team aware of the different versions of a process: what they think it is vs. what it actually is vs. what it should be vs. what it could be? <--- Score

80. What are the boundaries of the scope? What is in bounds and what is not? What is the start point? What is the stop point? <--- Score

81. Are there any constraints known that bear on the ability to perform Azure Databricks work? How is the team addressing them? <--- Score

Add up total points for this section: _____ = Total points for this section

Divided by: ______ (number of statements answered) = ______ Average score for this section

Transfer your score to the Azure Databricks Index at the beginning of the SelfAssessment.

CRITERION #3: MEASURE:

INTENT: Gather the correct data. Measure the current performance and evolution of the situation.

In my belief, the answer to this question is clearly defined:

5 Strongly Agree

4 Agree

3 Neutral

2 Disagree

1 Strongly Disagree

1. What analytic methods are needed to extract the value from big data? <--- Score

2. How does cognitive computing enable big data analytics? <--- Score

3. Big data analytics in supply chain: hype or here to stay? <--- Score

4. What is your customer centricity and how does analytics drive it? <--- Score

5. How does your organization utilize big data and analytics services? <--- Score

6. What is the appetite on the ground for talent analytics and big data? <--- Score

7. How are you ing it? <--- Score

8. Do other companies have a strategy for big data analytics? <--- Score

9. How frequently do you track Azure Databricks measures? <--- Score

10. Is data analytics embedded into every phase of Internal Audit? <--- Score

11. How will costs be allocated? <--- Score

12. What can big data and text analytics tell you about client experience and satisfaction? <--- Score

13. Do you do big data analytics on-premises? <--- Score

14. What is different about big data analytics? <--- Score

15. What is the business impact of this problem? <--- Score

16. How are big data analytics techniques applied in the procurement process? <--- Score

17. What role will big data analytics and ai play in the future of lean manufacturing? <--- Score

18. How do you analyze the massive amounts of data that become part of your big data infrastructure? <--- Score

19. Avoiding making decisions in a vacuum -- did the executive team set priorities with intimate with customers? <--- Score

20. What is the primary scope of the existing analytics strategy? <--- Score

21. Where is big data analytics applied in the procurement process? <--- Score

22. What problems can you solve with streaming analytics? <--- Score

23. Is an analytics building block selected? <--- Score

24. Why do you need agile analytics? <--- Score

25. Is there an opportunity to requirements? <--- Score

26. How is ai use in medical practice distinguished from big data analytics applications for health care delivery and population health? <--- Score

27. How satisfied are you with your vendor and product for big data analytics? <--- Score

28. Is databricks vying for a full analytics stack? <--- Score

29. How can companies best use big data analytics to drive supply chains? <--- Score

30. What is the impact of your customer engagement? <--- Score

31. How mature is your use of big data analytics? <--- Score

32. How do you sharpen your analytics competencies? <--- Score

33. How severe is the impact of garbage collection on performance of Spark based data analytics? <--- Score

34. How can azure technologies be leveraged best for enterprise analytics? <--- Score

35. What is the expected size of data that will be analyzed?

<--- Score

36. Have you made assumptions about the shape of the future, particularly its impact on your customers and competitors? <--- Score

37. Which data is available for analysis? <--- Score

38. What do people want to ? <--- Score

39. What are your tools for big Technology Value Creation data analytics? <--- Score

40. How would you rate your organizations skill at leveraging big data and advanced analytics to help with each aspects of innovation? <--- Score

41. How much data is enough to perform a statistical analysis? <--- Score

42. What are the hardware challenges you face/expect to face with your data analytics infrastructure? <--- Score

43. What analytic methods are needed to extract value from big data? <--- Score

44. How aware, committed and prepared are your business units on analytics and data governance? <--- Score

45. What are business s looking for when it comes to big data analysis? <--- Score

46. What is the right competence in Big Data Analytics? <--- Score

47. What tools do you use for big data analytics? <--- Score

48. Why customer analytics and journey design? <--- Score

49. What product(s) does your organization use with big data for BI/analytics? <--- Score

50. Descriptive analytics answers the questions about what happened in the past? <--- Score

51. Which benefits would ensue if your organization implemented some form of big data analytics? <--- Score

52. When should you integrate the outcome of your big data analysis with your data warehouse? <--- Score

53. How do you quantify and qualify impacts? <--- Score

54. Why big data analytics is applied in the procurement process? <--- Score

55. What were the reasons your organization decided for deploying a data analytics infrastructure? <--- Score

56. What type of data analytics applications does/will your organization primarily run? <--- Score

57. How can you measure Azure Databricks in a systematic way? <--- Score

58. How can big data analytics be integrated into the workflow? <--- Score

59. Can existing licenses be used to minimize costs? <--- Score

60. What are the estimated costs of proposed changes? <--- Score

61. What types of data sources can you take advantage of with big data analytics?

<--- Score

62. How do data analytics enhance cyber defence? <--- Score

63. Do you have enough information to or a conclusion? <--- Score

64. How and why does Big Data promise to bring benefits, such as economic prosperity and positive social impacts, across many sectors? <--- Score

65. Do you have advanced analytics scenarios on unusual datasets? <--- Score

66. Will the big data cloud environment enable lower total cost of ownership and better it staff productivity than alternative deployment models? <--- Score

67. What is measured? Why? <--- Score

68. Is your organization limited in the types of data analytics it can produce? <--- Score

69. Do open source technologies meet enterprise analytics needs? <--- Score

70. How should the analytics be continuously refined based on new data? <--- Score

71. How complete is the current analytics and data governance strategy, from your expectation standpoint? <--- Score

72. What makes cloud computing such a cost-effective delivery model for big data analytics? <--- Score

73. How can big data analytics be integrated into the clinical workflow? <--- Score

74. What is your strategy for developing analytics skill sets in your organization?

<--- Score

75. Why the journey to cloud-based analytics? <--- Score

76. Could costs be investigated and worked into next years budget? <--- Score

77. What can big data analytics tell your organization? <--- Score

78. Do you use any big data analytical tool? <--- Score

79. What kind of analysis can be performed by social media data? <--- Score

80. What evidence is there and what is measured? <--- Score

81. What are the privacy concerns that arise with big (personal) data and

the use of advanced analytics and how can corresponding be built into data security and privacy practices? <--- Score

82. How do customer analytics improve the journey design processes? <--- Score

83. Do you need to Iterate over data for analytics and reporting? <--- Score

84. Is security a priority for your vendors? <--- Score

85. How are big data Analytics techniques currently applied in the procurement process? <--- Score

86. Is tool evaluation for future state analytics technology and data management architecture part of this assignment? <--- Score

87. Why should you focus more on logical data warehouse than physical data warehouse?

<--- Score

88. What are the main components of architectures for analytics services? <--- Score

89. How does advanced analytics help you? <--- Score

90. Have you invested in specific tools for Big Data and Analytics? <--- Score

91. To dos and now you stick to three to five clear priorities for the coming year? <--- Score

92. How can you acquire more customers at less cost? <--- Score

93. Is file i/o detrimental to spark based data analytics performance? <--- Score

94. Why do you need a new engine and programming model for data analytics in the first place? <--- Score

95. Which customer experience dimensions most impact customer value and action across the interaction lifecycle? <--- Score

96. Are Azure Databricks vulnerabilities categorized and prioritized? <--- Score

97. What big-data technology is utilized by your speech analytics solution? <--- Score

98. What is the role for big data analytics in helping your organization find ways to detect fraud? <--- Score

99. What is changing in the realm of big data and analytics? <--- Score

100. Priorities for the quarter and how are you going to communicate them?

<--- Score

101. How will next-generation workloads such as containers and big-data analytics require your organization to consider digital transformation? <--- Score

102. How much value does analytics provide your organization and what budget constraints exist? <--- Score

103. What is agile analytics testing? <--- Score

104. On the path to big data analytics, what is a modern business intelligence platform to your organization? <--- Score

105. How much more could be achieved with big data analytics? <--- Score

106. In which areas are you using big data analytics today? <--- Score

107. How does data size affect the micro-architecture performance of Spark based data analytics? <--- Score

108. How big a challenge is privacy for the data analytics team? <--- Score

109. Do big data visualization data analytics capabilities matter? <--- Score

110. Cost – is the software open source or commercial? <--- Score

111. What tools & technologies are used currently to cater to analytics and data management needs? <--- Score

112. What are the benefits of big data analytics? <--- Score

113. Are transformational initiatives all about analytics and big data?

<--- Score

114. How is performance measured? <--- Score

115. What one or two analytics would help determine if a problem still exists or might be emerging? <--- Score

116. What type of tooling do you need to allow corresponding individuals to solely focus on writing a Spark application? <--- Score

117. Can systems accurately calculate and disseminate individual bill impacts to customers within the given launch window? <--- Score

118. What big data storage, processing, and analytics solutions are provided? <--- Score

119. Is the scope of governance in the consulting assignment limited to analytics governance?

<--- Score

120. Do you have a roap for customer analytics? <--- Score

121. What is the level of customization in your data analytics infrastructure? <--- Score

122. How can you consistently and efficiently deploy analytics built in different languages? <--- Score

123. Is sufficient time allowed for data analysis? <--- Score

124. Is it wise to focus as much attention and effort on Spark? <--- Score

125. What business challenges are you addressing with your data analytics deployment? <--- Score

126. Is need persistent enough to justify development costs? <--- Score

127. Is the glass for data, analytics, and AI in large organizations half empty or half full? <--- Score

128. How might big data research lessen the environmental impact of data analytics work? <--- Score

129. Do you use data analytics as a predictive tool or to report past performance? <--- Score

130. How does using Big Data technologies impact the daily IT Operations of your organization, aiming to benefit from them? <--- Score

Add up total points for this section: _____ = Total points for this section

Divided by: ______ (number of statements answered) = ______ Average score

for this section

Transfer your score to the Azure Databricks Index at the beginning of the SelfAssessment.

CRITERION #4: ANALYZE:

INTENT: Analyze causes, assumptions and hypotheses.

In my belief, the answer to this question is clearly defined:

5 Strongly Agree

4 Agree

3 Neutral

2 Disagree

1 Strongly Disagree

1. How structured are your datasets? <--- Score

2. Is the data stored in encrypted form?

<--- Score

3. How do you successfully execute a data migration project? <--- Score

4. Why is it that you are talking about data mining now? <--- Score

5. What is the value in Big Data? <--- Score

6. What risks are involved with the storage and retention of big datasets? <--- Score

7. Is micro-architecture performance invariant to input data size? <--- Score

8. Which data technology should you use? <--- Score

9. How should you provision the database instances?

<--- Score

10. What then does this have to say about the use and value of big data to business? <--- Score

11. Which file format should you use to receive the data? <--- Score

12. Which database and authorization types are used? <--- Score

13. Can you improve data quality? <--- Score

14. How will you document and map the data landscape to your long-term vision? <--- Score

15. Which technologies do you currently use a lot for streaming and/or complex event processing cases? <--- Score

16. What criteria should you look for in data access patterns and usage patterns? <--- Score

17. Do your employees have the opportunity to do what they do best everyday? <--- Score

18. How is this approach integrated into the innovation process? <--- Score

19. What are the capabilities of future big data practitioners? <--- Score

20. Do you have IoT type of data? <--- Score

21. What are the interfaces between Cyber-Physical Systems and Big Data Applications? <--- Score

22. Is big data a tool for inclusion or exclusion?

<--- Score

23. Where do the data live? <--- Score

24. What data is stored about employees and work? <--- Score

25. Why databricks delta? <--- Score

26. How exactly do you do data science and what does it mean? <--- Score

27. How do you promote understanding that opportunity for improvement is not criticism of the status quo, or the people who created the status quo? <--- Score

28. Can you use machine data to predict service needs or optimal configurations for customers? <--- Score

29. Is it possible to restore data from a previous date? <--- Score

30. What do you do with Big Data? <--- Score

31. How do you act on the data you need? <--- Score

32. Which tool should you use to configure a pipeline to copy data? <--- Score

33. Which database solution should you use to store the application data? <--- Score

34. What are the overall processes in Demand Forecasting? <--- Score

35. How important is big data to HR professionals? <--- Score

36. Have such new options for using data sounded the death knell for the Enterprise Data Warehouse? <--- Score

37. What can you do when the data gets big? <--- Score

38. What is the role of big data in procurement? <--- Score

39. What will your organization gain from the Azure Databricks PoC? <--- Score

40. Does all your data have value? <--- Score

41. How should you configure Azure Data Factory? <--- Score

42. Where is big data going as an industry to produce tangible value? <--- Score

43. How does big data the procurement process? <--- Score

44. What process is necessary to obtain this evidence? <--- Score

45. How much data movement are you willing to do? <--- Score

46. What kind of data will require what sort of treatment? <--- Score

47. Does big data technology include relational databases? <--- Score

48. What end points will provide the data? <--- Score

49. Does the data rate match your application? <--- Score

50. Is a data lake the right choice? <--- Score

51. Are you ready for a data lake? <--- Score

52. How is big data being applied in supply chain operations? <--- Score

53. How is your competition using big data? <--- Score

54. How do you work with data in Spark? <--- Score

55. Where does big data fit with other social business, e-commerce, or customer experience management initiatives? <--- Score

56. Where are the data located?

<--- Score

57. Can the data stored on a Cloud instance be encrypted? <--- Score

58. Is the data reliable? <--- Score

59. How do you manage various data types? <--- Score

60. Who needs data prep? <--- Score

61. What can azure do for your data? <--- Score

62. Are you using Spark as a processing engine? <--- Score

63. How do the different data sets relate to each other?

<--- Score

64. What happens in cases where the stream data delivery rate exceeds the potential data processing rate? <--- Score

65. Which data do you currently collect? <--- Score

66. Where is the data stored? <--- Score

67. How do you optimize your data collection? <--- Score

68. How do your work systems and key work processes relate to and capitalize on your core competencies? <--- Score

69. Do you have several teams involved in your processes? <--- Score

70. What good is quality data if it is not presented in a way that is meaningful or easily understood? <--- Score

71. What is so special about this particular data type that such longrespected guidelines no longer apply? <--- Score

72. How should you design the processing system? <--- Score

73. What database management systems do you use? <--- Score

74. Can you use Hive to process the data? <--- Score

75. How are the necessary changes to the national data protection framework introduced by the GDPR addressed in your country? <--- Score

76. What are the implications of big data for data security data breaches and identity fraud?

<--- Score

77. Big data: a tool for development or threat to privacy? <--- Score

78. What is the best data storage solution to recommend? <--- Score

79. How do you measure data sets? <--- Score

80. Does performance remain consistent as you enlarge the data size? <--- Score

81. What is your objective for using data? <--- Score

82. When to consider azure databricks? <--- Score

83. What are the differences when data are delete from a heap and clustered

index? <--- Score

84. Who can help you establish an effective working relationship with data? <--- Score

85. Who has a deep understanding of your data? <--- Score

86. Are you missing Azure Databricks opportunities? <--- Score

87. How many executor processes were used to run this job? <--- Score

88. Is your data being randomly sub-sampled? <--- Score

89. How can adopting big data in the procurement process improve procurement performance? <--- Score

90. What is possible given the data? <--- Score

91. How will the DSS fit into the decision-making process? <--- Score

92. What data points should be collected? <--- Score

93. How do you optimize the way in which data is consumed? <--- Score

94. What is all the fuss about the modern data warehouse? <--- Score

95. What are the key data sources in your business? <--- Score

96. How smart is your big data platform? <--- Score

97. How can you leverage customer profitability data to optimize distribution channel partnerships? <--- Score

98. How do you make a query run in less time than it takes to look at the data? <--- Score

99. What controls or clearance levels does Microsoft have for the personnel that have DevOps access to cloud environments or physical access to data centers? <--- Score

100. Has your organization defined a big data strategy? <--- Score

101. How can you create a Hive table to access data in object storage? <--- Score

102. What other organizational variables, such as reward systems or communication systems, affect the performance of this Azure Databricks process? <--- Score

103. How will the processing authorization procedure (if any) be affected by the implementation of GDPR? <--- Score

104. Why is databricks becoming popular? <--- Score

105. What is the right data to look for upstream, from apps and tools? <--- Score

106. Does file i/o become a bottleneck under large data volumes? <--- Score

107. Can spark streaming be used for typical request-response kind of processing (like web services)? <--- Score

108. How do others see cloud playing into Big Data? <--- Score

109. How can the business bring existing data assets to life and give s

tools some actually want to use? <--- Score

110. What is the purpose of data if there are no insights derived from it? <--- Score

111. What about getting intelligent results from unusual data sources? <--- Score

112. What type of data consumption do you ? <--- Score

113. How do you ensure that the Azure Databricks opportunity is realistic? <--- Score

114. What database management systems do you use in production? <--- Score

115. Which data processing solution should you recommend? <--- Score

116. Who can originate the loans, and how does the process work? <--- Score

117. Do your data team have the business acumen to shutdown invalid hypothesis in a timely manner? <--- Score

118. What technologies are required to route data through your networks? <--- Score

119. Which data technology should you implement? <--- Score

120. Data governance: does your organization have a data governance model, and how effective is data governance with the big data program? <--- Score

121. What advantage does big data bring? <--- Score

122. What are the unique considerations of retaining data for extended periods of time?

<--- Score

123. What if your data file does not have a schema? <--- Score

124. How have customers been delighted as a result of big data insights? <--- Score

125. Can metadata provide information about the author of a file? <--- Score

126. Where value creation is one directional, what value do you create in the customer buying process? <--- Score

127. How have your customers been delighted as a result of the insights you have gained from big data? <--- Score

128. What systems/processes must you excel at? <--- Score

129. How is big data in combination with human insights applied in developing more nuanced and applicable consumer decision journeys? <--- Score

130. How should retention and disposal of the data be handled? <--- Score

131. Can you trust your data? <--- Score

132. What parts of the decision-making process will be ed by the system? <--- Score

133. How do you efficiently aggregate assorted customer data in an effective manner? <--- Score

134. How should you configure the Clean Missing Data module? <--- Score

135. What kind of big data do you work with?

<--- Score

136. What exactly is a data scientist, or even data science for that matter? <--- Score

137. Are data workflows flexible enough? <--- Score

138. How can big data contribute to a customer intimacy strategy? <--- Score

139. What data mining algorithms do you use at work? <--- Score

140. What tangible benefits do you hope to achieve through your big data initiatives? <--- Score

141. Scope: to what extent does the big data program all parts of your organization and all potential s? <--- Score

142. What if you want to run data transformation code in Python? <--- Score

Add up total points for this section: _____ = Total points for this section

Divided by: ______ (number of statements answered) = ______ Average score for this section

Transfer your score to the Azure Databricks Index at the beginning of the SelfAssessment.

CRITERION #5: IMPROVE:

INTENT: Develop a practical solution. Innovate, establish and test the solution and to measure the results.

In my belief, the answer to this question is clearly defined:

5 Strongly Agree

4 Agree

3 Neutral

2 Disagree

1 Strongly Disagree

1. How did the team generate the list of possible solutions? <--- Score

2. What tools were used to evaluate the potential solutions? <--- Score

3. Comparing corresponding results to the customers desired outcomes or goals, which recommendation is best? <--- Score

4. How do you publish it so that others can read it and reproduce your results in a hundred years time? <--- Score

5. Who makes the Azure Databricks decisions in your organization? <--- Score

6. Can you integrate quality management and risk management? <--- Score

7. How do you measure risk? <--- Score

8. How have your customers been delighted as a result of the insights you have gained?

<--- Score

9. Do those selected for the Azure Databricks team have a good general understanding of what Azure Databricks is all about? <--- Score

10. What communications are necessary to the implementation of the solution? <--- Score

11. Were any criteria developed to assist the team in testing and evaluating potential solutions? <--- Score

12. What error proofing will be done to address some of the discrepancies observed in the ‘as is’ process? <--- Score

13. Is there a high likelihood that any recommendations will achieve their intended results? <--- Score

14. What storage solution should you use in the design?

<--- Score

15. Do you really understand how your business customers buy? <--- Score

16. How do you manage and improve your Azure Databricks work systems to deliver customer value and achieve organizational success and sustainability? <--- Score

17. What lessons, if any, from a pilot were incorporated into the design of the full-scale solution? <--- Score

18. Is a contingency plan established? <--- Score

19. Which programming language is optimum to build the control logic? <--- Score

20. How do you improve cross-organizational information sharing for broader connected intelligence? <--- Score

21. Given your business strategy and objectives, what do you want to understand about customers? <--- Score

22. How can the phases of Azure Databricks development be identified? <--- Score

23. Are possible solutions generated and tested? <--- Score

24. Do you really understand your customers? <--- Score

25. How scalable is your Azure Databricks solution? <--- Score

26. What is the role of government in workforce development? <--- Score

27. Is the optimal solution selected based on testing and analysis?

<--- Score

28. Describe the design of the pilot and what tests were conducted, if any? <--- Score

29. What decisions need ? <--- Score

30. Are new and improved process (‘should be’) maps developed? <--- Score

31. Developing service-based business models: which innovation capability for which innovation dimensions? <--- Score

32. Are improved process (‘should be’) maps modified based on pilot data and analysis? <--- Score

33. What does the ‘should be’ process map/design look like? <--- Score

34. Is your development staff ready for artificial intelligence? <--- Score

35. What problem and resolution might occur? <--- Score

36. How do you evaluate security and privacy risk? <--- Score

37. How does the solution remove the key sources of issues discovered in the analyze phase? <--- Score

38. Are there any constraints (technical, political, cultural, or otherwise) that would inhibit certain solutions? <--- Score

39. What attendant changes will need to be made to ensure that the solution is successful? <--- Score

40. Does the solution meet the goal?

<--- Score

41. Is a solution implementation plan established, including schedule/work breakdown structure, resources, risk management plan, cost/budget, and control plan? <--- Score

42. How are Azure Databricks risks managed? <--- Score

43. What is the result of running the command? <--- Score

44. Do you have clearly defined objectives before beginning development on your motion graphic? Do you want to highlight content, spark action, or encourage awareness of an issue? <--- Score

45. What Azure Databricks improvements can be made? <--- Score

46. What is the team’s contingency plan for potential problems occurring in implementation?

<--- Score

47. How do you mitigate the risks associated with digital disruption? <--- Score

48. How will the group know that the solution worked? <--- Score

49. What exactly can you expect from the map function? <--- Score

50. How will you know that a change is an improvement? <--- Score

51. How will the team or the process owner(s) monitor the implementation plan to see that it is working as intended? <--- Score

52. Was a pilot designed for the proposed solution(s)? <--- Score

53. Which vendor is primarily in the lead to coordinate your IoT Solution development? <--- Score

54. How easily will solutions move to new markets? <--- Score

55. Does spark streaming java or any other technologies as part of development? <--- Score

56. Is there a cost/benefit analysis of optimal solution(s)? <--- Score

57. What were the underlying assumptions on the cost-benefit analysis? <--- Score

58. Who will be using the results of the measurement activities? <--- Score

59. How risky is your organization? <--- Score

60. What is the Azure Databricks’s sustainability risk? <--- Score

61. How does a nuanced and applicable consumer decision journey look in practice? <--- Score

62. Are the best solutions selected? <--- Score

63. Is there a small-scale pilot for proposed improvement(s)? What conclusions were drawn from the outcomes of a pilot? <--- Score

64. Is the implementation plan designed? <--- Score

65. How do you understand and improve the long-term health of your customer relationships? <--- Score

66. What is the implementation plan? <--- Score

67. Do you understand who the consumers are? <--- Score

68. How do you mitigate Azure Databricks risk? <--- Score

69. What tools were used to tap into the creativity and encourage ‘outside the box’ thinking? <--- Score

70. What tools were most useful during the improve phase? <--- Score

71. How significant is the improvement in the eyes of the end ? <--- Score

72. What is Azure Databricks’s impact on utilizing the best solution(s)? <--- Score

73. What should you use to build the solution? <--- Score

74. Do you get the same results from the different sources? <--- Score

75. Does attacking the biggest challenge carry the biggest risk of failure? <--- Score

76. Is pilot data collected and analyzed? <--- Score

Add up total points for this section: _____ = Total points for this section

Divided by: ______ (number of statements answered) = ______ Average score for this section

Transfer your score to the Azure Databricks Index at the beginning of the SelfAssessment.

CRITERION #6: CONTROL:

INTENT: Implement the practical solution. Maintain the performance and correct possible complications.

In my belief, the answer to this question is clearly defined:

5 Strongly Agree

4 Agree

3 Neutral

2 Disagree

1 Strongly Disagree

1. What is involved in machine learning? <--- Score

2. What other systems, operations, processes, and infrastructures (hiring practices, staffing, training, incentives/rewards, metrics/dashboards/scorecards, etc.) need updates, additions, changes, or deletions in order to facilitate knowledge transfer and improvements? <--- Score

3. Have new or revised work instructions resulted? <--- Score

4. What key inputs and outputs are being measured on an ongoing basis? <--- Score

5. What other areas of the group might benefit from the Azure Databricks team’s improvements, knowledge, and learning? <--- Score

6. What can you learn in a connected lighting test bed? <--- Score

7. Are suggested corrective/restorative actions indicated on the response plan for known causes to problems that might surface? <--- Score

8. Are documented procedures clear and easy to follow for the operators? <--- Score

9. Does the Azure Databricks performance meet the customer’s requirements? <--- Score

10. Is there a documented and implemented monitoring plan? <--- Score

11. Is a response plan in place for when the input, process, or output measures indicate an ‘out-of-control’ condition? <--- Score

12. How will new or emerging customer needs/requirements be checked/communicated to orient the process toward meeting the new specifications and continually reducing variation? <--- Score

13. Are operating procedures consistent? <--- Score

14. Is there documentation that will the successful operation of the improvement?

<--- Score

15. Which machine learning service application should you use? <--- Score

16. Will any special training be provided for results interpretation? <--- Score

17. Can the existing data sources be utilised to make effective plans? <--- Score

18. What are the critical parameters to watch? <--- Score

19. Are you going to use a data warehouse scale (big scale) technology, or will data mart scale technology be sufficient? <--- Score

20. Are new process steps, standards, and documentation ingrained into normal operations? <--- Score

21. Are you ready and willing to learn different development patterns and/or new technologies? <--- Score

22. How can the standards ecosystem help in Big Data analytics in of deploy reusable analytics? <--- Score

23. Can from partners be adjusted? <--- Score

24. Are there documented procedures? <--- Score

25. What are managements plans for using big data and analytics for auditing, compliance, and risk management now and in the future? <--- Score

26. Is there a standardized process? <--- Score

27. Do you have future plans to leverage big data (hadoop/spark/etc.) or nosql in addition to transactional sources?

<--- Score

28. How do you scale this service out? <--- Score

29. How can azure connect, secure, manage, monitor, and control your devices in the cloud? <--- Score

30. Which metric should you monitor? <--- Score

31. Is there a control plan in place for sustaining improvements (short and longterm)? <--- Score

32. Where do ideas that reach policy makers and planners as proposals for Azure Databricks strengthening and reform actually originate? <--- Score

33. What planning and preparation evidence can be located? <--- Score

34. How will input, process, and output variables be checked to detect for suboptimal conditions? <--- Score

35. How will report readings be checked to effectively monitor performance? <--- Score

36. What can deep learning do for you? <--- Score

37. Is new knowledge gained imbedded in the response plan? <--- Score

38. How do you scale your single-tenant services out? <--- Score

39. How does it use ai and machine learning? <--- Score

40. Will the big data cloud environment scale-out, shared-nothing massively parallel processing, storage optimization, dynamic query

optimization, and mixed workload management better than alternative deployment models (e.g., on-premises appliances, software on commodity hardware)? <--- Score

41. What are the primary use cases for deploying or planning to deploy data analytics? <--- Score

42. Who is the Azure Databricks process owner? <--- Score

43. What are the key elements of your Azure Databricks performance improvement system, including your evaluation, organizational learning, and innovation processes? <--- Score

44. How likely is it that a particular approach will allow the solution to scale elastically in volume, velocity, and variety as Big Data analytics requirements evolve? <--- Score

45. What quality tools were useful in the control phase? <--- Score

46. How will the day-to-day responsibilities for monitoring and continual improvement be transferred from the improvement team to the process owner? <--- Score

47. Will existing staff require re-training, for example, to learn new business processes? <--- Score

48. What are analytics/data mining/machine learning? <--- Score

49. Do you provide a monitoring service for your customers to add value? <--- Score

50. Does the existing data governance plan have any directions provided for analytics? <--- Score

51. Does your organization execute advanced analytics against big data today? <--- Score

52. While deg a new system are there any adjustments to the traditional architecture for inclusion of big data in the future enhancements? <--- Score

53. Does job training on the documented procedures need to be part of the process team’s education and training? <--- Score

54. Where do you start and how do you plan for big data analytics? <--- Score

55. How could you use Machine Learning? <--- Score

56. Do all machines have guards to protect against points of operation, nip points, rotating parts, moving parts, flying chips, sparks, etc.? <--- Score

57. How might the group capture best practices and lessons learned so as to leverage improvements? <--- Score

58. How would you monitor the Spark job submitted by you as well as entire Spark cluster? <--- Score

59. How likely is it that a particular approach will meet requirements for istration, monitoring, and optimization of the Big Data platform/service over its expected useful life? <--- Score

60. What can machine learning be used for? <--- Score

61. What should the next improvement project be that is related to Azure Databricks? <--- Score

62. Who can access to Control Plane ? <--- Score

63. Which machine learning algorithm should you use? <--- Score

64. What is the control/monitoring plan?

<--- Score

65. How will the process owner improvement in present and future sigma levels, process capabilities? <--- Score

66. Is a response plan established and deployed? <--- Score

67. Has the improved process and its steps been standardized? <--- Score

68. Do you effectively react on findings from big data, machine learning and AI? <--- Score

69. Are there any best practices or standards for the use of Big Data solutions? <--- Score

70. What do you measure to effectiveness gains? <--- Score

71. Do spark based data analytics benefit from using larger scale-up servers? <--- Score

72. Is reporting being used or needed? <--- Score

73. How will the process owner and team be able to hold the gains? <--- Score

74. Does the response plan contain a definite closed loop continual improvement scheme (e.g., plan-do-check-act)? <--- Score

75. What are world-class organizations doing to meet the high demands of customers? What can you learn and apply to your organization? <--- Score

76. What is the recommended frequency of auditing? <--- Score

77. How should you configure monitoring? <--- Score

78. Is there a recommended audit plan for routine surveillance inspections of Azure Databricks’s gains? <--- Score

79. Is knowledge gained on process shared and institutionalized? <--- Score

80. Is your monitoring tool intuitive? <--- Score

81. Is there a transfer of ownership and knowledge to process owner and process team tasked with the responsibilities. <--- Score

82. Which cloud resources will be monitored? <--- Score

83. Does a troubleshooting guide exist or is it needed? <--- Score

84. Can you scale this business solution? <--- Score

85. What languages/libraries/frameworks do you use for data science and machine learning? <--- Score

Add up total points for this section: _____ = Total points for this section

Divided by: ______ (number of statements answered) = ______ Average score for this section

Transfer your score to the Azure Databricks Index at the beginning of the SelfAssessment.

CRITERION #7: SUSTAIN:

INTENT: Retain the benefits.

In my belief, the answer to this question is clearly defined:

5 Strongly Agree

4 Agree

3 Neutral

2 Disagree

1 Strongly Disagree

1. What are the challenges from a storage standpoint that Spark poses to both s and to the IT teams that them? <--- Score

2. Are there any financial or monetary ties to the computer? <--- Score

3. How can design elements and technology applications across projects be consistent and coordinated? <--- Score

4. Who is on the team? <--- Score

5. How do you represent the node IDs? <--- Score

6. Which storage type should you use for each file type? <--- Score

7. Who creates the information in the record (the or computer)? <--- Score

8. How can pricing be implemented in a coherent, system approach? <--- Score

9. Azure stack where to deploy, and when? <--- Score

10. What does the communication do? <--- Score

11. How many customers are transferred to another agent in incoming calls? <--- Score

12. If you weren’t already in this business, would you enter it today? And if not, what are you going to do about it? <--- Score

13. How are advances in digital technology changing the way you work, your industry and your community? <--- Score

14. Where should the already stated technologies run? <--- Score

15. What kind of projects are expected in the future? <--- Score

16. What are the benefits of using CSV or CSR? <--- Score

17. What does customer centricity actually mean? <--- Score

18. Who are your new customers? <--- Score

19. Which conditions are necessary? <--- Score

20. How satisfied are your customers with your current offerings? <--- Score

21. How might you actually apply this technology to your business? <--- Score

22. How do you find out how your customers perceive the value of what you sell? <--- Score

23. How can you become more high-tech but still be high touch? <--- Score

24. Have to manually place services on hosts what about node failure? <--- Score

25. How does the copy activity work? <--- Score

26. How can you increase engagement? <--- Score

27. Do you have a single customer view? <--- Score

28. Have benefits been optimized with all key stakeholders? <--- Score

29. What are your most important goals for the strategic Azure Databricks objectives? <--- Score

30. Why value co-creation? <--- Score

31. How can azure help secure your app? <--- Score

32. Did you have something to introduce into the record? <--- Score

33. Why does it matter? <--- Score

34. Is running spark jobs on kubernetes a good idea? <--- Score

35. Are you willing to go all the way to deliver on a specific brand promise; and are you willing to do something that appears customer unfriendly to

help fund it? <--- Score

36. Which customers are more likely to go into arrears? <--- Score

37. Can your organization get to know it is customers better? <--- Score

38. And the best indications from your own intelligence? <--- Score

39. What was the last experiment you ran? <--- Score

40. What does a commit actually involve? <--- Score

41. What is safety-critical software, and how can ada and spark help? <--- Score

42. Where do you see your organization today? <--- Score

43. Are you listening to your customers? <--- Score

44. How can stressed employees deliver better customer service? <--- Score

45. What goals must be served by whatever action is taken? <--- Score

46. How actively do customers use your products? <--- Score

47. What is the business purpose for the records? <--- Score

48. Which version of the technologies/Kubernetes is being offered? <--- Score

49. Which type of linked service should you use? <--- Score

50. Are you paying enough attention to the partners your company depends on to succeed? <--- Score

51. Were lessons learned captured and communicated? <--- Score

52. How ready are you to go there? <--- Score

53. What is going to happen if the prosumer disappears? <--- Score

54. Playing not to lose vs. playing to win -- were you generally playing defense and have now gone of the offensive? <--- Score

55. How many participants were involved? <--- Score

56. How should you complete the command? <--- Score

57. How is social responsibility important in your organizations marketing? <--- Score

58. What does this concept win in the end? <--- Score

59. Why is knowledge management important? <--- Score

60. How should you configure the storage ? <--- Score

61. What are you challenging? <--- Score

62. Which kinds of storage are available? <--- Score

63. Can you do all this work? <--- Score

64. How can azure deploy your services? <--- Score

65. Who provided input into determining what continuous auditing tools are being built and/or used? <--- Score

66. Do you share your organizations values? <--- Score

67. Are prior peak levels now reachable? <--- Score

68. Will employees respond appropriately? <--- Score

69. What engine(s) do you want to use?

<--- Score

70. Where are you now? <--- Score

71. Does the cloud provider keep an eye on current threats and take action? <--- Score

72. Which objects storage-engines are offered? <--- Score

73. Why is mainstream As a Service hard? <--- Score

74. What are you using Spark for? <--- Score

75. At what stage are your customers most satisfied with your brand? <--- Score

76. What customer groups are meaningful?

<--- Score

77. Which offers are more likely to be the most compelling for specific customers? <--- Score

78. What is the overall business strategy? <--- Score

79. What offers should be presented to a customer? <--- Score

80. How should you configure authentication and authorization for deployments? <--- Score

81. Which customers should you include in a campaign to maximize response rates? <--- Score

82. What key disruptive technologies or business models do you see? <--- Score

83. When will the customer move to a new customer segment? <--- Score

84. Which components should you use for each Elastic pool job services task? <--- Score

85. What is the sentiment for Spark, and what is driving interest in the technology? <--- Score

86. What contribution does it make to business success? <--- Score

87. What is the best way to achieve the goal? <--- Score

88. What unique value proposition (UVP) do you offer? <--- Score

89. Which environment should you use?

<--- Score

90. Have you ever had a security incident with one of your applications? <--- Score

91. What do we do when new problems arise? <--- Score

92. What does a subscription look like to a customer? <--- Score

93. How can coworking spaces retain s? <--- Score

94. Which technologies are being provided/ed? <--- Score

95. Have you achieved the customer insight and engagement that you set out to? <--- Score

96. Is there a global public sphere? <--- Score

97. What happens after you submit the job? <--- Score

98. What are your dashboards, cockpits, scorecards? <--- Score

99. Are there resources that are missing? <--- Score

100. What is the best recommendation to achieve the goal? <--- Score

101. How have your customers been delighted? <--- Score

102. Which are the most frequent journeys? <--- Score

103. How do you run your application on a Spark cluster? <--- Score

104. Is there any existing Azure Databricks governance structure? <--- Score

105. Who would you most like to hear from? <--- Score

106. What is the significance of a value exchange protocol? <--- Score

107. What was the pain point or market insight that sparked the idea for your product? <--- Score

108. Which technologies should you use? <--- Score

109. Where do you find the Spark logs? <--- Score

110. When to consider a virtual machine? <--- Score

111. How do you install custom Python libraries through the node bootstrap? <--- Score

112. What should ai mean to your organization? <--- Score

113. How and when can customer journeys be effectively influenced? <--- Score

114. How is spark driving the insight economy forward? <--- Score

115. Are new benefits received and understood? <--- Score

116. Is there an image build service?

<--- Score

117. Can bare-metal servers be deployed via the cloud? <--- Score

118. Can it work alongside office 365? <--- Score

119. What kind of customers are having a negative outcome? <--- Score

120. Have new benefits been realized? <--- Score

121. When is a customer likely to leave? <--- Score

122. Which customers are most likely to make a purchase in the future, what are they likely to buy, and why? <--- Score

123. How should you complete the code segment? <--- Score

124. What does it mean to be a Cloud organization? <--- Score

125. Is it deeply integrated with other services? <--- Score

126. Which azure services should you use? <--- Score

127. What is your competitive advantage? <--- Score

128. Are loyalty schemes a manifestation of relationship marketing? <--- Score

129. How do you transition from the baseline to the target? <--- Score

130. How can azure integrate ai into your app? <--- Score

131. Are you running the right tools for customer intelligence? <--- Score

132. Where will you find your talent? <--- Score

133. How can you know your customers better? <--- Score

134. What freight is mostly likely to spark interest at borders? <--- Score

135. Have you seen this pattern before? <--- Score

136. Who are your unknown customers? <--- Score

137. How do you keep moving forward? <--- Score

138. Should you change your medication? <--- Score

139. Do you have a filter through which you put customer suggestions? <--- Score

140. Did you miss previous insights? <--- Score

141. Can it integrate with your organizations existing tools? <--- Score

142. Which operating systems are ed? <--- Score

143. How do you leverage the changes in technology for how you do business today? <--- Score

144. What are the anticipated defenses and the governments responses? <--- Score

145. How are insurance companies responding to this? <--- Score

146. To which customers and products will which actions relate? <--- Score

147. How many channels are your customers using? <--- Score

148. Do you use any tools for workflow management? <--- Score

149. What about currency of releases? <--- Score

150. How do you build dashboards and reports that are fast and reliable? <--- Score

151. What are the touchpoints where customers can acquire your products and services? <--- Score

152. What are the difficulties marketing faces with this approach? <--- Score

153. Why azure for knowledge mining? <--- Score

154. Do your competitors have more insight about your customers than you do? <--- Score

155. Where do you run spark? <--- Score

156. Which languages do you use spark in? <--- Score

157. What are the advantages of this?

<--- Score

158. How are you going to train them? <--- Score

159. Which azure tool should you use? <--- Score

160. How well are you prepared to use it? <--- Score

161. How do you write Spark programs? <--- Score

162. When should you use Spark? <--- Score

163. What makes spark fast? <--- Score

164. How does it fit into your organizations security orchestration?

<--- Score

165. Which technology should you use for each task? <--- Score

166. Which container name and item name should you use? <--- Score

167. How do you maintain Azure Databricks’s Integrity? <--- Score

168. Which security technologies should you use? <--- Score

169. How will this affect the marketing of companies? <--- Score

170. Is there a work around that you can use? <--- Score

171. Are your loyalty schemes a manifestation of relationship marketing?

<--- Score

172. How do people become customers? <--- Score

173. Which logging technology is used? <--- Score

174. Are sales practices in the best interest of your customers? <--- Score

175. How do you convince people in your organization who do not work in the marketing department that the customer is the boss? <--- Score

176. What is the kind of project structure that would be appropriate for your Azure Databricks project, should it be formal and complex, or can it be less formal and relatively simple? <--- Score

177. What has been done by your organization to motivate you to work well? <--- Score

178. Are competitors obtaining sharper, more timely insights? <--- Score

179. How do you gain consumers trust? <--- Score

180. Is it not just a lot of IF statements? <--- Score

181. Does the cloud platform provide a Logging as a Service functionality? <--- Score

182. What if you want to use the built-in streaming functionality within Spark streaming? <--- Score

183. How and when does customer orientation influence frontline employee job outcomes? <--- Score

184. Which technology should you implement?

<--- Score

185. What if you want to classify images or more complex patterns, and use a black box approach to classification? <--- Score

186. What is different this time around? <--- Score

187. How or when is the record created? <--- Score

188. Putting the customer at the center of attention, is that the basis of your marketing approach? <--- Score

189. What happens when systems approach the level of human intelligence? <--- Score

190. Does marketing automation hurt or help customer intimacy? <--- Score

191. What should you include in the design? <--- Score

192. How do you update when there is constant usage? <--- Score

193. What does success looks like? <--- Score

194. How do you spend time/$ to engage? <--- Score

195. Who are your known customers? <--- Score

196. What is knowledge management? <--- Score

197. What is business intelligence? <--- Score

198. How does Azure Databricks integrate with other stakeholder initiatives? <--- Score

199. What is going on behind the scenes? <--- Score

200. How can you get more customers like your best customer? <--- Score

201. What is the right balance between a highly interactive experience for end s and governance for IT? <--- Score

202. What was the overall engagement rate? <--- Score

203. How will you motivate the stakeholders with the least vested interest? <--- Score

204. How do you balance your budgets to keep both your customers AND your management happy? <--- Score

205. Is a storage class useable? <--- Score

206. Can you afford to keep everything? <--- Score

207. How widespread is the use of blockchain in the enterprise now? <--- Score

208. What type of customer intelligence do you receive today? <--- Score

209. How does co-creation and personalization work from a content perspective? <--- Score

210. How is spark ed in azure hdinsight? <--- Score

211. Which option or options should you configure?

<--- Score

212. Which technology should you use? <--- Score

213. How should you configure the lifecycle policy? <--- Score

214. Which operating systems are offered by the provider with which versions? <--- Score

215. Do loyal customers rely on the website and mobile app experience more before or after making a successful reservation? <--- Score

216. What happens, with the Jobs once you submit the job on cluster? <--- Score

217. Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Azure Databricks, how do you gain traction? <--- Score

218. What do you want to know about your customers? <--- Score

219. When do you get to the finish line? <--- Score

220. What is your strategy for protecting your brand(s) in the marketplace? <--- Score

221. Where is a single customer view important? <--- Score

222. How do you manage the journey? <--- Score

223. Can the cloud show/provide the patch level of existing machines? <--- Score

224. What is your customer lifetime value? <--- Score

225. How are you using AI in your program? <--- Score

226. What should you implement? <--- Score

227. What are artificial neural networks? <--- Score

228. When would you choose Spark? <--- Score

229. Is your knowledge applicable to this new technology? <--- Score

230. Which product do you want to use when? <--- Score

231. What is the role of government in research? <--- Score

232. Which customers use multiple products? <--- Score

233. Is the impact that Azure Databricks has shown? <--- Score

234. What is the customer looking for in interaction with the brand at each specific point? <--- Score

235. Does the interface function as intended? <--- Score

236. What are the key benefits of blockchain for the enterprise? <--- Score

237. How can you incorporate to ensure safe and effective use of Azure Databricks into the services that you provide? <--- Score

238. Do you have more than one server to manage?

<--- Score

239. What is it like to work for you? <--- Score

240. Customers can see savings from day one, and pay off the project over ? <--- Score

241. How much time does it take to provide the container service? <--- Score

242. What have been your experiences in defining long range Azure Databricks goals? <--- Score

243. How do you provide a safe environment -physically and emotionally? <--- Score

244. Where will the backup be stored? <--- Score

245. Does the cloud provide a managed patch service? <--- Score

246. Can images be created from existing cloud instances? <--- Score

247. What other insights can you derive about your consumer and market segments? <--- Score

248. What Azure Databricks modifications can you make work for you? <--- Score

249. Is proof of stake good for anything? <--- Score

Add up total points for this section: _____ = Total points for this section

Divided by: ______ (number of statements answered) = ______ Average score for this section

Transfer your score to the Azure Databricks Index at the beginning of the Self-

Assessment.

Azure Databricks and Managing Projects, Criteria for Project Managers:

1.0 Initiating Process Group: Azure Databricks

1. How well did you do?

2. What communication items need improvement?

3. What input will you be required to provide the Azure Databricks project team?

4. Did the Azure Databricks project team have the right skills?

5. Specific - is the objective clear in of what, how, when, and where the situation will be changed?

6. What are the inputs required to produce the deliverables?

7. How will you know you did it?

8. Were decisions made in a timely manner?

9. What do you need to do?

10. What will you do to minimize the impact should a risk event occur?

11. Are identified risks being monitored properly, are new risks arising during the Azure Databricks project or are foreseen risks occurring?

12. Who are the Azure Databricks project stakeholders?

13. Did the Azure Databricks project team have the right skills?

14. What are the pressing issues of the hour?

15. What were the challenges that you encountered during the execution of a previous Azure Databricks project that you would not want to repeat?

16. Do you know the roles & responsibilities required for this Azure Databricks project?

17. What were things that you did very well and want to do the same again on the next Azure Databricks project?

18. During which stage of Risk planning are risks prioritized based on

probability and impact?

19. What technical work to do in each phase?

20. First of all, should any action be taken?

1.1 Project Charter: Azure Databricks

21. Why Outsource?

22. How do you manage integration?

23. Where does all this information come from?

24. What outcome, in measureable , are you hoping to accomplish?

25. Are there special technology requirements?

26. Who is the Azure Databricks project Manager?

27. Market – identify products market, including whether it is outside of the objective: what is the purpose of the program or Azure Databricks project?

28. What does it need to do?

29. Does the Azure Databricks project need to consider any special capacity or

capability issues?

30. How much?

31. What are the known stakeholder requirements?

32. What metrics could you look at?

33. What is the most common tool for helping define the detail?

34. Why do you manage integration?

35. Who are the stakeholders?

36. What material?

37. Why executive ?

38. What is in it for you?

39. When is a charter needed?

40. Fit with other Products Compliments – Cannibalizes?

1.2 Stakeholder : Azure Databricks

41. How should employers make voices heard?

42. What are the major Azure Databricks project milestones requiring communications or providing communications opportunities?

43. What & Why?

44. What opportunities exist to provide communications?

45. Who is managing stakeholder engagement?

46. How much influence do they have on the Azure Databricks project?

47. Is your organization ready for change?

48. How will reports be created?

49. Who wants to talk about Security?

50. How big is the gap?

51. What is the power of the stakeholder?

1.3 Stakeholder Analysis Matrix: Azure Databricks

52. How will the Azure Databricks project benefit them?

53. Which conditions out of the control of the management are crucial to contribute for the achievement of the development objective?

54. Is changing technology threatening your organizations position?

55. How to measure the achievement of the Development Objective?

56. Opponents; who are the opponents?

57. What is social & public ability ?

58. Participatory approach: how will key stakeholders participate in the Azure Databricks project?

59. What are the mechanisms of public and social ability, and how can they be made better?

60. Advantages of proposition?

61. Cashflow, start-up cash-drain?

62. What tools would help you communicate?

63. How do you manage Azure Databricks project Risk?

64. What could your organization improve?

65. Usps (unique selling points)?

66. Reliability of data, plan predictability?

67. Who determines value?

68. Cultural, attitudinal, behavioural?

69. Price, value, quality?

70. Do recommendations include actions to address any differential distribution

of impacts?

71. Which conditions out of the control of the management are crucial for the achievement of the outputs?

2.0 Planning Process Group: Azure Databricks

72. How are the principles of aid effectiveness (ownership, alignment, management for development results and mutual responsibility) being applied in the Azure Databricks project?

73. Are the follow-up indicators relevant and do they meet the quality needed to measure the outputs and outcomes of the Azure Databricks project?

74. Why is it important to determine activity sequencing on Azure Databricks projects?

75. How can you make your needs known?

76. What type of estimation method are you using?

77. How well will the chosen processes produce the expected results?

78. Is your organization showing technical capacity and leadership commitment to keep working with the Azure Databricks project and to repeat it?

79. To what extent do the intervention objectives and strategies of the Azure

Databricks project respond to your organizations plans?

80. What is involved in Azure Databricks project scope management, and why is good Azure Databricks project scope management so important on information technology Azure Databricks projects?

81. Is the identification of the problems, inequalities and gaps, with respective causes, clear in the Azure Databricks project?

82. What should you do next?

83. What is the NEXT thing to do?

84. Why do it Azure Databricks projects fail?

85. Have more efficient (sensitive) and appropriate measures been adopted to respond to the political and socio-cultural problems identified?

86. What makes your Azure Databricks project successful?

87. Are there efficient coordination mechanisms to avoid overloading the counterparts, participating stakeholders?

88. If a risk event occurs, what will you do?

89. What do they need to know about the Azure Databricks project?

90. To what extent have the target population and participants made the activities own, taking an active role in it?

91. To what extent are the visions and actions of the partners consistent or divergent with regard to the program?

2.1 Project Management Plan: Azure Databricks

92. Is the budget realistic?

93. Are cost risk analysis methods applied to develop contingencies for the estimated total Azure Databricks project costs?

94. Is mitigation authorized or recommended?

95. What is risk management?

96. Are calculations and results of analyzes essentially correct?

97. What worked well?

98. If the Azure Databricks project management plan is a comprehensive document that guides you in Azure Databricks project execution and control, then what should it NOT contain?

99. What is the justification?

100. What did not work so well?

101. Are there any windfall benefits that would accrue to the Azure Databricks project sponsor or other parties?

102. Are comparable cost estimates used for comparing, screening and selecting alternative plans, and has a reasonable cost estimate been developed for the recommended plan?

103. What data/reports/tools/etc. do program managers need?

104. What are the training needs?

105. What are the assigned resources?

106. Is the engineering content at a feasibility level-of-detail, and is it sufficiently complete, to provide an adequate basis for the baseline cost estimate?

107. Who is the Azure Databricks project Manager?

108. What are the assumptions?

109. What data/reports/tools/etc. do your PMs need?

2.2 Scope Management Plan: Azure Databricks

110. What strengths do you have?

111. Function of the configuration control board?

112. Is there a scope management plan that includes how Azure Databricks project scope will be defined, developed, monitored, validated and controlled?

113. Are milestone deliverables effectively tracked and compared to Azure Databricks project plan?

114. Do you keep stake holders informed?

115. When will scope verification be performed?

116. Where do scope management processes fit in?

117. Will the Azure Databricks project deliverables become accepted in writing?

118. Are tasks tracked by hours?

119. Does the title convey to the reader the essence of the Azure Databricks project?

120. Without-plan conditions?

121. Has process improvement efforts been completed before requirements efforts begin?

122. Are the Azure Databricks project plans updated on a frequent basis?

123. Are trade-offs between accepting the risk and mitigating the risk identified?

124. Is an industry recognized mechanized tool(s) being used for Azure Databricks project scheduling & tracking?

125. Are there checklists created to demine if all quality processes are followed?

126. Are you meeting with stake holders and team ?

127. Has allowance been made for vacations, holidays, training (learning time for each team member), staff promotions & staff turnovers?

2.3 Requirements Management Plan: Azure Databricks

128. Could inaccurate or incomplete requirements in this Azure Databricks project create a serious risk for the business?

129. Is there formal agreement on who has authority to request a change in requirements?

130. Is stakeholder risk tolerance an important factor for the requirements process in this Azure Databricks project?

131. Are actual resources expenditures versus planned expenditures acceptable?

132. Has the requirements team been instructed in the Change Control process?

133. Will the product release be stable and mature enough to be deployed in the community?

134. What went wrong?

135. Who came up with this requirement?

136. The wbs is developed as part of a t planning session. and how do you know that youhave done this right?

137. Is it new or replacing an existing business system or process?

138. After the requirements are gathered and set forth on the requirements , theyre little more than a laundry list of items. Some may be duplicates, some might conflict with others and some will be too broad or too vague to understand. Describe how the requirements will be analyzed. Who will perform the analysis?

139. Describe the process for rejecting the Azure Databricks project requirements. Who has the authority to reject Azure Databricks project requirements?

140. How will the requirements become prioritized?

141. Does the Azure Databricks project have a Change Control process?

142. What is a problem?

143. Why manage requirements?

144. Did you distinguish the scope of work the contractor(s) will be required to do?

145. Which hardware or software, related to, or as outcome of the Azure Databricks project is new to your organization?

146. How knowledgeable is the team in the proposed application area?

147. What are you counting on?

2.4 Requirements Documentation: Azure Databricks

148. Consistency. are there any requirements conflicts?

149. Who is involved?

150. How does the proposed Azure Databricks project contribute to the overall objectives of your organization?

151. How can you document system requirements?

152. Verifiability. can the requirements be checked?

153. What are the potential disadvantages/advantages?

154. Is the requirement properly understood?

155. Has requirements gathering uncovered information that would necessitate changes?

156. Completeness. are all functions required by the customer included?

157. What images does it conjure?

158. What is the risk associated with the technology?

159. How much does requirements engineering cost?

160. Can the requirements be checked?

161. If applicable; are there issues linked with the fact that this is an offshore Azure Databricks project?

162. Is new technology needed?

163. How linear / iterative is your Requirements Gathering process (or will it be)?

164. Do your constraints stand?

165. What are the acceptance criteria?

166. Who is interacting with the system?

167. What is the risk associated with cost and schedule?

2.5 Requirements Traceability Matrix: Azure Databricks

168. Why use a WBS?

169. How do you manage scope?

170. What are the chronologies, contingencies, consequences, criteria?

171. Do you have a clear understanding of all subcontracts in place?

172. Is there a requirements traceability process in place?

173. How small is small enough?

174. Why do you manage scope?

175. What percentage of Azure Databricks projects are producing traceability matrices between requirements and other work products?

176. Will you use a Requirements Traceability Matrix?

177. Describe the process for approving requirements so they can be added to the traceability matrix and Azure Databricks project work can be performed. Will the Azure Databricks project requirements become approved in writing?

178. What is the WBS?

179. How will it affect the stakeholders personally in career?

2.6 Project Scope Statement: Azure Databricks

180. Has the Azure Databricks project scope statement been reviewed as part of the baseline process?

181. Relevant - ask yourself can you get there; why are you doing this Azure Databricks project?

182. Change management vs. change leadership - what is the difference?

183. What are the major deliverables of the Azure Databricks project?

184. Will this process be communicated to the customer and Azure Databricks project team?

185. Azure Databricks project lead, team lead, solution architect?

186. Is the Azure Databricks project manager qualified and experienced in Azure Databricks project management?

187. Is the plan for your organization of the Azure Databricks project resources adequate?

188. How often will scope changes be reviewed?

189. What is a process you might recommend to the accuracy of the research deliverable?

190. Are there adequate Azure Databricks project control systems?

191. Will the qa related information be reported regularly as part of the status reporting mechanisms?

192. Is there a Quality Assurance Plan documented and filed?

193. Is there a baseline plan against which to measure progress?

194. Have the reports to be produced, distributed, and filed been defined?

195. Has a method and process for requirement tracking been developed?

196. Is your organization structure appropriate for the Azure Databricks projects size and complexity?

197. Has the format for tracking and monitoring schedules and costs been defined?

198. Do you anticipate new stakeholders ing the Azure Databricks project over time?

2.7 Assumption and Constraint Log: Azure Databricks

199. Does the document/deliverable meet all requirements (for example, statement of work) specific to this deliverable?

200. Is the definition of the Azure Databricks project scope clear; what needs to be accomplished?

201. Does the Azure Databricks project have a formal Azure Databricks project Plan?

202. No superfluous information or marketing narrative?

203. What threats might prevent you from getting there?

204. What would you gain if you spent time working to improve this process?

205. Are requirements management tracking tools and procedures in place?

206. If it is out of compliance, should the process be amended or should the Plan be amended?

207. Have all necessary approvals been obtained?

208. Are there ways to reduce the time it takes to get something approved?

209. Contradictory information between different documents?

210. How can you prevent/fix violations?

211. Is the process working, and people are not executing in compliance of the process?

212. Can you perform this task or activity in a more effective manner?

213. Are there procedures in place to effectively manage interdependencies with other Azure Databricks projects / systems?

214. Are you meeting your customers expectations consistently?

215. Were the system requirements formally reviewed prior to initiating the design phase?

216. What does an audit system look like?

217. Have the scope, objectives, costs, benefits and impacts been communicated to all involved and/or impacted stakeholders and work groups?

218. Contradictory information between document sections?

2.8 Work Breakdown Structure: Azure Databricks

219. How much detail?

220. Who has to do it?

221. Why is it useful?

222. What has to be done?

223. Is the work breakdown structure (wbs) defined and is the scope of the Azure Databricks project clear with assigned deliverable owners?

224. Is it still viable?

225. When do you stop?

226. How will you and your Azure Databricks project team define the Azure Databricks projects scope and work breakdown structure?

227. Is it a change in scope?

228. Can you make it?

229. How big is a work-package?

230. Where does it take place?

231. What is the probability that the Azure Databricks project duration will exceed xx weeks?

232. How far down?

233. When does it have to be done?

2.9 WBS Dictionary: Azure Databricks

234. The anticipated business volume?

235. Is each control assigned to a single organizational element directly responsible for the work and identifiable to a single element of the CWBS?

236. Are procedures in existence that control replanning of unopened work packages, and are corresponding procedures adhered to?

237. Should you have a test for each code module?

238. Identify potential or actual overruns and underruns?

239. Are authorized changes being incorporated in a timely manner?

240. Is data disseminated to the contractors management timely, accurate, and usable?

241. Does the scheduling system identify in a timely manner the status of work?

242. Is work progressively subdivided into detailed work packages as requirements are defined?

243. The already stated responsible for overhead performance control of related costs?

244. What went right?

245. Are management actions taken to reduce indirect costs when there are significant adverse variances?

246. Identify and isolate causes of favorable and unfavorable cost and schedule variances?

247. Are budgets or values assigned to work packages and planning packages in of dollars, hours, or other measurable units?

248. Are data elements reconcilable between internal summary reports and reports forwarded to us?

249. Are data being used by managers in an effective manner to ascertain Azure Databricks project or functional status, to identify reasons or significant variance, and to initiate appropriate corrective action?

250. Does the scheduling system provide for the identification of work progress

against technical and other milestones, and also provide for forecasts of completion dates of scheduled work?

251. Does the contractors system provide unit costs, equivalent unit or lot costs in of labor, material, other direct, and indirect costs?

252. Performance to date and material commitment?

253. Contemplated overhead expenditure for each period based on the best information currently available?

2.10 Schedule Management Plan: Azure Databricks

254. Are meeting objectives identified for each meeting?

255. Are metrics used to evaluate and manage Vendors?

256. Has a sponsor been identified?

257. Alignment to strategic goals & objectives?

258. Is the communication plan being followed?

259. Does the Azure Databricks project have quality set of schedule BOEs?

260. Are there any activities or deliverables being added or gold-plated that could be dropped or scaled back without falling short of the original requirement?

261. Will rolling way planning be used?

262. Are vendor contract reports, reviews and visits conducted periodically?

263. Are the schedule estimates reasonable given the Azure Databricks project?

264. Are there checklists created to determine if all quality processes are followed?

265. Are written status reports provided on a designated frequent basis?

266. Pareto diagrams, statistical sampling, flow charting or trend analysis used quality monitoring?

267. Has a quality assurance plan been developed for the Azure Databricks project?

268. Is a process defined for baseline approval and control?

269. Why conduct schedule analysis?

270. Define units of measurement for each resource. For example, are you referencing gallons or liters?

271. Is quality monitored from the perspective of the customers needs and

expectations?

2.11 Activity List: Azure Databricks

272. How detailed should a Azure Databricks project get?

273. What are the critical bottleneck activities?

274. What is the probability the Azure Databricks project can be completed in xx weeks?

275. For other activities, how much delay can be tolerated?

276. How difficult will it be to do specific activities on this Azure Databricks project?

277. How should ongoing costs be monitored to try to keep the Azure Databricks project within budget?

278. When do the individual activities need to start and finish?

279. What is the LF and LS for each activity?

280. Is there anything planned that does not need to be here?

281. Who will perform the work?

282. Should you include sub-activities?

283. What is the total time required to complete the Azure Databricks project if no delays occur?

284. Where will it be performed?

285. Can you determine the activity that must finish, before this activity can start?

286. What did not go as well?

287. How do you determine the late start (LS) for each activity?

288. Are the required resources available or need to be acquired?

2.12 Activity Attributes: Azure Databricks

289. Which method produces the more accurate cost assignment?

290. Are the required resources available?

291. Activity: what is Missing?

292. Can more resources be added?

293. How do you manage time?

294. How else could the items be grouped?

295. What is the general pattern here?

296. What activity do you think you should spend the most time on?

297. What is missing?

298. Has management defined a definite timeframe for the turnaround or Azure Databricks project window?

299. Why?

300. Do you feel very comfortable with your prediction?

301. Would you consider either of corresponding activities an outlier?

302. How difficult will it be to do specific activities on this Azure Databricks project?

303. How much activity detail is required?

304. Have constraints been applied to the start and finish milestones for the phases?

305. Does your organization of the data change its meaning?

306. How difficult will it be to complete specific activities on this Azure Databricks project?

2.13 Milestone List: Azure Databricks

307. Environmental effects?

308. Who will manage the Azure Databricks project on a day-to-day basis?

309. Competitive advantages?

310. Marketing - reach, distribution, awareness?

311. Loss of key staff?

312. Milestone pages should display the ID of the person who added the milestone. Does a report or query exist that provides this audit information?

313. New USPs?

314. What specific improvements did you make to the Azure Databricks project proposal since the previous time?

315. Do you foresee any technical risks or developmental challenges?

316. How late can the activity start?

317. Can you derive how soon can the whole Azure Databricks project finish?

318. It is to be a narrative text providing the crucial aspects of your Azure Databricks project proposal answering what, who, how, when and where?

319. What is the market for your technology, product or service?

320. Political effects?

321. Obstacles faced?

322. How will the milestone be verified?

323. What date will the task finish?

324. What is your organizations history in doing similar activities?

325. Continuity, supply chain robustness?

2.14 Network Diagram: Azure Databricks

326. Review the logical flow of the network diagram. Take a look at which activities you have first and then sequence the activities. Do they make sense?

327. What must be completed before an activity can be started?

328. If a current contract exists, can you provide the vendor name, contract start, and contract expiration date?

329. Exercise: what is the probability that the Azure Databricks project duration will exceed xx weeks?

330. Planning: who, how long, what to do?

331. What job or jobs could run concurrently?

332. What controls the start and finish of a job?

333. Which type of network diagram allows you to depict four types of dependencies?

334. What activities must occur simultaneously with this activity?

335. Where do schedules come from?

336. Are you on time?

337. What are the tools?

338. What activities must follow this activity?

339. Will crashing x weeks return more in benefits than it costs?

340. Why must you schedule milestones, such as reviews, throughout the Azure Databricks project?

341. What is the completion time?

342. What to do and When?

2.15 Activity Resource Requirements: Azure Databricks

343. When does monitoring begin?

344. Why do you do that?

345. Are there unresolved issues that need to be addressed?

346. Other in specific areas?

347. Anything else?

348. Do you use tools like decomposition and rolling-wave planning to produce the activity list and other outputs?

349. Organizational Applicability?

350. Which logical relationship does the PDM use most often?

351. What are constraints that you might find during the Human Resource Planning process?

352. What is the Work Plan Standard?

353. How many signatures do you require on a check and does this match what is in your policy and procedures?

354. How do you handle petty cash?

355. Time for overtime?

2.16 Resource Breakdown Structure: Azure Databricks

356. Which resource planning tool provides information on resource responsibility and ability?

357. Who will use the system?

358. What is the difference between % Complete and % work?

359. Which resources should be in the resource pool?

360. What defines a successful Azure Databricks project?

361. What is the number one predictor of a groups productivity?

362. Goals for the Azure Databricks project. What is each stakeholders desired outcome for the Azure Databricks project?

363. Why do you do it?

364. What can you do to improve productivity?

365. How difficult will it be to do specific activities on this Azure Databricks project?

366. What is the primary purpose of the human resource plan?

367. Why time management?

368. What is the purpose of asg and documenting responsibility?

369. What are the requirements for resource data?

370. The list could probably go on, but, the thing that you would most like to know is, How long & How much?

371. What is each stakeholders desired outcome for the Azure Databricks project?

2.17 Activity Duration Estimates: Azure Databricks

372. Briefly describe some key events in the history of Azure Databricks project management. What Azure Databricks project was the first to use modern Azure Databricks project management?

373. Are changes to the scope managed according to defined procedures?

374. Are updates on work results collected and used as inputs to the performance reporting process?

375. After how many days will the lease cost be the same as the purchase cost for the equipment?

376. What is pmp certification, and why do you think the number of people earning it has grown so much in the past ten years?

377. What are the three main outputs of quality control?

378. Which skills do you think are most important for an information technology Azure Databricks project manager?

379. Do scope statements include the Azure Databricks project objectives and expected deliverables?

380. Will the new application negatively affect the current IT infrastructure?

381. Are procedures defined for calculating cost estimates?

382. How does the job market and current state of the economy affect human resource management?

383. What is the BEST thing to do?

384. Find an example of a contract for information technology services. Analyze the key features of the contract. What type of contract was used and why?

385. How can organizations use a weighted decision matrix to evaluate proposals as part of source selection?

386. How much time is required to develop it?

387. What are the Azure Databricks project management deliverables of each process group?

388. What are two suggestions for ensuring adequate change control on Azure Databricks projects that involve outside contracts?

389. What are some crucial elements of a good Azure Databricks project plan?

390. Why is there a new or renewed interest in the field of Azure Databricks project management?

391. What are the advantages and disadvantages of PERT?

2.18 Duration Estimating Worksheet: Azure Databricks

392. Science = process: the scientific method?

393. Why estimate costs?

394. What is an Average Azure Databricks project?

395. Why estimate time and cost?

396. Does the Azure Databricks project provide innovative ways for stakeholders to overcome obstacles or deliver better outcomes?

397. What is your role?

398. Do any colleagues have experience with your organization and/or RFPs?

399. Can the Azure Databricks project be constructed as planned?

400. Is a construction detail attached (to aid in explanation)?

401. What info is needed?

402. Done before proceeding with this activity or what can be done concurrently?

403. What utility impacts are there?

404. How should ongoing costs be monitored to try to keep the Azure Databricks project within budget?

405. Value pocket identification & quantification what are value pockets?

406. What is the total time required to complete the Azure Databricks project if no delays occur?

407. Will the Azure Databricks project collaborate with the local community and leverage resources?

408. What work will be included in the Azure Databricks project?

2.19 Project Schedule: Azure Databricks

409. If you can not fix it, how do you do it differently?

410. Eliminate unnecessary activities. Are there activities that came from a template or previous Azure Databricks project that are not applicable on this phase of this Azure Databricks project?

411. Is the Azure Databricks project schedule available for all Azure Databricks project team to review?

412. Are activities connected because logic dictates the order in which others occur?

413. How do you use schedules?

414. Understand the constraints used in preparing the schedule. Are activities connected because logic dictates the order in which others occur?

415. How closely did the initial Azure Databricks project Schedule compare with the actual schedule?

416. Why is software Azure Databricks project disaster so common?

417. Did the Azure Databricks project come in on schedule?

418. How do you manage Azure Databricks project Risk?

419. Are procedures defined by which the Azure Databricks project schedule may be changed?

420. How much slack is available in the Azure Databricks project?

421. What is Azure Databricks project management?

422. Meet requirements?

423. Are key risk mitigation strategies added to the Azure Databricks project schedule?

424. Schedule/cost recovery?

425. Master Azure Databricks project schedule?

426. To what degree is do you feel the entire team was committed to the Azure Databricks project schedule?

2.20 Cost Management Plan: Azure Databricks

427. Has the Azure Databricks project manager been identified?

428. Is the Azure Databricks project sponsor clearly communicating the business case or rationale for why this Azure Databricks project is needed?

429. Is your organization certified as a supplier, wholesaler, regular dealer, or manufacturer of corresponding products/supplies?

430. Are vendor invoices audited for accuracy before payment?

431. Have the key functions and capabilities been defined and assigned to each release or iteration?

432. Is there an approved case?

433. Schedule preparation – how will the schedules be prepared during each phase of the Azure Databricks project?

434. Are enough systems & personnel assigned to the Azure Databricks project?

435. Have process improvement efforts been completed before requirements efforts begin?

436. Designated small business reserve?

437. Is a payment system in place with proper reviews and approvals?

438. What would the life cycle costs be?

439. Have the key elements of a coherent Azure Databricks project management strategy been established?

440. Are change requests logged and managed?

441. What weaknesses do you have?

442. Have all involved Azure Databricks project stakeholders and work groups committed to the Azure Databricks project?

443. Is the steering committee active in Azure Databricks project oversight?

444. Do all stakeholders know how to access this repository and where to find the Azure Databricks project documentation?

445. Cost variances – how will cost variances be identified and corrected?

446. Has a capability assessment been conducted?

2.21 Activity Cost Estimates: Azure Databricks

447. What do you want to know about the stay to know if costs were inappropriately high or low?

448. Where can you get activity reports?

449. In which phase of the acquisition process cycle does source qualifications reside?

450. Does the estimator estimate by task or by person?

451. What happens if you cannot produce the documentation for the single audit?

452. When do you enter into PPM?

453. Will you use any tools, such as Azure Databricks project management software, to assist in capturing Earned Value metrics?

454. How do you allocate indirect costs to activities?

455. What are you looking for?

456. What is the Azure Databricks projects sustainability strategy that will ensure Azure Databricks project results will endure or be sustained?

457. Were the tasks or work products prepared by the consultant useful?

458. How do you manage cost?

459. How do you treat istrative costs in the activity inventory?

460. Review – what are some common errors in activities to avoid?

461. How and when do you enter into Azure Databricks project Procurement Management?

462. One way to define activities is to consider how organization employees describe jobs to families and friends. You basically want to know, What do you do?

463. How many activities should you have?

464. How Award?

465. Can you change your activities?

2.22 Cost Estimating Worksheet: Azure Databricks

466. Does the Azure Databricks project provide innovative ways for stakeholders to overcome obstacles or deliver better outcomes?

467. Is it feasible to establish a control group arrangement?

468. What happens to any remaining funds not used?

469. What will others want?

470. Who is best positioned to know and assist in identifying corresponding factors?

471. Will the Azure Databricks project collaborate with the local community and leverage resources?

472. What can be included?

473. What is the purpose of estimating?

474. What is the estimated labor cost today based upon this information?

475. What additional Azure Databricks project(s) could be initiated as a result of this Azure Databricks project?

476. Can a trend be established from historical performance data on the selected measure and are the criteria for using trend analysis or forecasting methods met?

477. Is the Azure Databricks project responsive to community need?

478. How will the results be shared and to whom?

479. Ask: are others positioned to know, are others credible, and will others cooperate?

480. Identify the timeframe necessary to monitor progress and collect data to determine how the selected measure has changed?

481. What costs are to be estimated?

2.23 Cost Baseline: Azure Databricks

482. Has the Azure Databricks projected annual cost to operate and maintain the product(s) or service(s) been approved and funded?

483. What deliverables come first?

484. How difficult will it be to do specific tasks on the Azure Databricks project?

485. Has the appropriate access to relevant data and analysis capability been granted?

486. Definition of done can be traced back to the definitions of what are you providing to the customer in of deliverables?

487. When should cost estimates be developed?

488. Why do you manage cost?

489. Vac -variance at completion, how much over/under budget do you expect to be?

490. Have the resources used by the Azure Databricks project been reassigned to other units or Azure Databricks projects?

491. Are procedures defined by which the cost baseline may be changed?

492. How accurate do cost estimates need to be?

493. Is request in line with priorities?

494. Should a more thorough impact analysis be conducted?

495. Will the Azure Databricks project fail if the change request is not executed?

496. What is the most important thing to do next to make your Azure Databricks project successful?

497. What does a good WBS NOT look like?

498. What is the consequence?

2.24 Quality Management Plan: Azure Databricks

499. How are calibration records kept?

500. How will you know that a change is actually an improvement?

501. Is this a Requirement?

502. How are data handled when a test is not run per specification?

503. Are you meeting the quality standards?

504. Written by multiple authors and in multiple writing styles?

505. What data do you gather/use/compile?

506. How do senior leaders create an environment that encourages learning and innovation?

507. Were there any deficiencies / issues in prior years self-assessment?

508. What are your key performance measures/indicators for tracking progress relative to your action plans?

509. Have adequate resources been provided by management to ensure Azure Databricks project success?

510. What process do you use to minimize errors, defects, and rework?

511. Is the amount of effort justified by the anticipated value of forming a new process?

512. How is staff trained?

513. What is the Difference Between a QMP and QAPP?

514. How do you decide who is responsible for g the data reports?

515. Are there unnecessary steps that are creating bottlenecks and/or causing people to wait?

516. How are training records kept?

2.25 Quality Metrics: Azure Databricks

517. Did evaluation start on time?

518. What happens if you get an abnormal result?

519. How are requirements conflicts resolved?

520. When is the security analysis testing complete?

521. Can you correlate your quality metrics to profitability?

522. When will the Final Guidance will be issued?

523. Is quality culture a competitive advantage?

524. What group is empowered to define quality requirements?

525. Was the overall quality better or worse than previous products?

526. Are documents on hand to provide explanations of privacy and confidentiality?

527. Is there a set of procedures to capture, analyze and act on quality metrics?

528. Was material distributed on time?

529. What method of measurement do you use?

530. Do the operators focus on determining; is there anything you need to worry about?

531. What can manufacturing professionals do to ensure quality is seen as an integral part of the entire product lifecycle?

532. What are your organizations next steps?

533. How do you communicate results and findings to upper management?

534. If the defect rate during testing is substantially higher than that of the previous release (or a similar product), then ask: Did you plan for and actually improve testing effectiveness?

535. What is the timeline to meet your goal?

536. How does one achieve stability?

2.26 Process Improvement Plan: Azure Databricks

537. Has the time line required to move measurement results from the points of collection to databases or s been established?

538. Have the frequency of collection and the points in the process where measurements will be made been determined?

539. What personnel are the coaches for your initiative?

540. Does explicit definition of the measures exist?

541. What is quality and how will you ensure it?

542. Where do you want to be?

543. What is the return on investment?

544. Are you following the quality standards?

545. What personnel are the change agents for your initiative?

546. How do you manage quality?

547. Have storage and access mechanisms and procedures been determined?

548. Are you making progress on the goals?

549. What lessons have you learned so far?

550. Purpose of goal: the motive is determined by asking, why do you want to achieve this goal?

551. Are there forms and procedures to collect and record the data?

552. Are you making progress on your improvement plan?

553. Have the ing tools been developed or acquired?

554. Where are you now?

2.27 Responsibility Assignment Matrix: Azure Databricks

555. Azure Databricks projected economic escalation?

556. What expertise is available in your department?

557. Will too many Communicating responsibilities tangle the Azure Databricks project in unnecessary communications?

558. Are your organizations and items of cost assigned to each pool identified?

559. Changes in the nature of the overhead requirements?

560. Are overhead costs budgets established on a basis consistent with anticipated direct business base?

561. The total budget for the contract (including estimates for authorized and unpriced work)?

562. Is every g-off responsibility and every communicating responsibility critically necessary?

563. Are estimates of costs at completion generated in a rational, consistent manner?

564. Time-phased control budgets?

565. Budgets assigned to control s?

566. Is work properly classified as measured effort, LOE, or apportioned effort and appropriately separated?

567. Are material costs reported within the same period as that in which BCWP is earned for that material?

568. What is the business need?

569. How can this help you with team building?

570. Is the anticipated (firm and potential) business base Azure Databricks projected in a rational, consistent manner?

571. Detailed schedules which control and work package start and completion dates/events?

572. What do you do when people do not respond?

2.28 Roles and Responsibilities: Azure Databricks

573. Does your vision/mission a culture of quality data?

574. What should you highlight for improvement?

575. Is the data complete?

576. Was the expectation clearly communicated?

577. Do you take the time to clearly define roles and responsibilities on Azure Databricks project tasks?

578. What are your major roles and responsibilities in the area of performance measurement and assessment?

579. Have you ever been a part of this team?

580. What expectations were met?

581. Do the values and practices inherent in the culture of your organization foster or hinder the process?

582. Required skills, knowledge, experience?

583. Key conclusions and recommendations: Are conclusions and recommendations relevant and acceptable?

584. What is working well within your organizations performance management system?

585. Implementation of actions: Who are the responsible units?

586. Are the quality assurance functions and related roles and responsibilities clearly defined?

587. Concern: where are you limited or have no authority, where you can not influence?

588. What should you do now to prepare yourself for a promotion, increased responsibilities or a different job?

589. Authority: what areas/Azure Databricks projects in your work do you have the authority to decide upon and act on the already stated decisions?

590. Attainable / achievable: the goal is attainable; can you actually accomplish the goal?

591. Who is responsible for implementation activities and where will the functions, roles and responsibilities be defined?

2.29 Human Resource Management Plan: Azure Databricks

592. Are actuals compared against estimates to analyze and correct variances?

593. Have lessons learned been conducted after each Azure Databricks project release?

594. Are all key components of a Quality Assurance Plan present?

595. How do you determine what key skills and talents are needed to meet the objectives. Is your organization primarily focused on a specific industry?

596. Is your organization primarily focused on a specific industry?

597. Are multiple estimation methods being employed?

598. Was the Azure Databricks project schedule reviewed by all stakeholders and formally accepted?

599. Are corrective actions and variances reported?

600. Who needs training?

601. Has your organization readiness assessment been conducted?

602. Who are the people that make up your organization and whom create the success that your organization enjoys as a whole?

603. Have adequate resources been provided by management to ensure Azure Databricks project success?

604. Are issues raised, assessed, actioned, and resolved in a timely and efficient manner?

605. Are all resource assumptions documented?

606. What areas were overlooked on this Azure Databricks project?

607. Is there an onboarding process in place?

2.30 Communications Management Plan: Azure Databricks

608. Which team member will work with each stakeholder?

609. What is Azure Databricks project communications management?

610. Who did you turn to if you had questions?

611. What communications method?

612. How do you manage communications?

613. Which stakeholders are thought leaders, influences, or early adopters?

614. In your work, how much time is spent on stakeholder identification?

615. Is the stakeholder role recognized by your organization?

616. Are there common objectives between the team and the stakeholder?

617. Do you feel more overwhelmed by stakeholders?

618. Who will use or be affected by the result of a Azure Databricks project?

619. Do you then often overlook a key stakeholder or stakeholder group?

620. Who were proponents/opponents?

621. Are there too many who have an interest in some aspect of your work?

622. What is the stakeholders level of authority?

623. Why is stakeholder engagement important?

624. Conflict resolution -which method when?

625. Do you ask; can you recommend others for you to talk with about this initiative?

626. What steps can you take for a positive relationship?

627. What approaches to you feel are the best ones to use?

2.31 Risk Management Plan: Azure Databricks

628. Where do risks appear in the business phases?

629. Are staff committed for the duration of the product?

630. Do you have a mechanism for managing change?

631. Do requirements put excessive performance constraints on the product?

632. What would you do differently?

633. Was an original risk assessment/risk management plan completed?

634. What things are likely to change?

635. Costs associated with late delivery or a defective product?

636. Should the risk be taken at all?

637. Do benefits and chances of success outweigh potential damage if success is not attained?

638. Do you have a consistent repeatable process that is actually used?

639. For software; does the software interface with new or unproven hardware or unproven vendor products?

640. Is the customer technically sophisticated in the product area?

641. Market risk: will the new product be useful to your organization or marketable to others?

642. Has something like this been done before?

643. How is the audit profession changing?

644. Does the Azure Databricks project team have experience with the technology to be implemented?

645. What is the likelihood?

646. Which is an input to the risk management process?

647. What can go wrong?

2.32 Risk : Azure Databricks

648. Is further information required before making a decision?

649. Are there any knock-on effects/impact on any of the other areas?

650. What can be done about it?

651. Do you require further engagement?

652. What should the audit role be in establishing a risk management process?

653. Are there any gaps in the evidence?

654. What is the reason for current performance gaps and do the risks and opportunities identified previously for this?

655. What is your current and future risk profile?

656. What should you do now?

657. How well are risks controlled?

658. How often will the Risk Management Plan and Risk be formally reviewed, and by whom?

659. Are your objectives at risk?

660. Risk probability and impact: how will the probabilities and impacts of risk items be assessed?

661. Who needs to know about this?

662. Who is going to do it?

663. Cost/benefit – how much will the proposed mitigations cost and how does this cost compare with the potential cost of the risk event/situation should it occur?

664. Schedule impact/severity estimated range (workdays) assume the event happens, what is the potential impact?

665. What are the major risks facing the Azure Databricks project?

666. What further options might be available for responding to the risk?

667. What are you going to do to limit the Azure Databricks projects risk exposure due to the identified risks?

2.33 Probability and Impact Assessment: Azure Databricks

668. Are people attending meetings and doing work?

669. How is the risk management process used in practice?

670. How much risk do others need to take?

671. How do you maximize short-term return on investment?

672. What risks are necessary to achieve success?

673. Have staff received necessary training?

674. Do the people have the right combinations of skills?

675. Are some people working on multiple Azure Databricks projects?

676. How carefully have the potential competitors been identified?

677. Risk urgency assessment -which of your risks could occur soon, or require a longer planning time?

678. What is the likely future demand of the customer?

679. Are flexibility and reuse paramount?

680. How is the Azure Databricks project going to be managed?

681. Is the delay in one subAzure Databricks project going to affect another?

682. Which role do you have in the Azure Databricks project?

683. What will be the likely political situation during the life of the Azure Databricks project?

684. How do you define a risk?

685. Can the Azure Databricks project proceed without assuming the risk?

2.34 Probability and Impact Matrix: Azure Databricks

686. What are the current demands of the customer?

687. What will be the likely incidence of conflict with neighboring Azure Databricks projects?

688. Maximize short-term return on investment?

689. Are there new risks that mitigation strategies might introduce?

690. During Azure Databricks project executing, a major problem occurs that was not included in the risk . What should you do FIRST?

691. Economic to take on the Azure Databricks project?

692. Are you working on the right risks?

693. What should be the gestation period for the Azure Databricks project with this technology?

694. How much is the probability of the risk occurring?

695. Which role do you have in the Azure Databricks project?

696. Have you ascribed a level of confidence to every critical technical objective?

697. Workarounds are determined during which risk management process?

698. Are enough people available?

699. Is the present organizational structure for handling the Azure Databricks project sufficient?

700. What can you use the analyzed risks for?

701. What are the levels of understanding of the future s of this technology?

702. Mandated specific features?

703. Which should be probably done NEXT?

2.35 Risk Data Sheet: Azure Databricks

704. What will be the consequences if it happens?

705. What do people affected think about the need for, and practicality of preventive measures?

706. What is the environment within which you operate (social trends, economic, community values, broad based participation, national directions etc.)?

707. Who has a vested interest in how you perform as your organization (our stakeholders)?

708. Has a sensitivity analysis been carried out?

709. What are you trying to achieve (Objectives)?

710. During work activities could hazards exist?

711. What are your core values?

712. What can happen?

713. What if client refuses?

714. Will revised controls lead to tolerable risk levels?

715. What were the Causes that contributed?

716. Potential for recurrence?

717. What can you do?

718. How can it happen?

719. What actions can be taken to eliminate or remove risk?

720. What are the main threats to your existence?

721. Risk of what?

722. What is the chance that it will happen?

2.36 Procurement Management Plan: Azure Databricks

723. Is the quality assurance team identified?

724. Are post milestone Azure Databricks project reviews (PMPR) conducted with your organization at least once a year?

725. Is Azure Databricks project work proceeding in accordance with the original Azure Databricks project schedule?

726. Are assumptions being identified, recorded, analyzed, qualified and closed?

727. Does the schedule include Azure Databricks project management time and change request analysis time?

728. Are milestone deliverables effectively tracked and compared to Azure Databricks project plan?

729. Are estimating assumptions and constraints captured?

730. Are updated Azure Databricks project time & resource estimates reasonable

based on the current Azure Databricks project stage?

731. Are status reports received per the Azure Databricks project Plan?

732. Are Azure Databricks project logs kept up to date?

733. Does the resource management plan include a personnel development plan?

734. Do Azure Databricks project managers participating in the Azure Databricks project know the Azure Databricks projects true status first hand?

735. Are key risk mitigation strategies added to the Azure Databricks project schedule?

736. Is documentation created for communication with the suppliers and Vendors?

737. Are all payments made according to the contract(s)?

738. Was your organizations estimating methodology being used and followed?

739. What are things that you need to improve?

2.37 Source Selection Criteria: Azure Databricks

740. What past performance information should be requested?

741. Which contract type places the most risk on the seller?

742. What will you use to capture evaluation and subsequent documentation?

743. What is the last item a Azure Databricks project manager must do to finalize Azure Databricks project close-out?

744. Can you reasonably estimate total organization requirements for the coming year?

745. Have team been adequately trained?

746. How should oral presentations be evaluated?

747. In order of importance, which evaluation criteria are the most critical to the determination of your overall rating?

748. What source selection software is your team using?

749. Has all proposal data been loaded?

750. What does an evaluation address and what does a sample resemble?

751. What evidence should be provided regarding proposal evaluations?

752. How should the oral presentations be handled?

753. How do you facilitate evaluation against published criteria?

754. How will you decide an evaluators write up is sufficient?

755. What is the basis of an estimate and what assumptions were made?

756. Does an evaluation need to include the identification of strengths and weaknesses?

757. Who is entitled to a debriefing?

758. Are resultant proposal revisions allowed?

759. In the technical/management area, what criteria do you use to determine the final evaluation ratings?

2.38 Stakeholder Management Plan: Azure Databricks

760. Detail warranty and/or maintenance periods?

761. Has the business need been clearly defined?

762. What is the drawback in using qualitative Azure Databricks project selection techniques?

763. Are there standards for code development?

764. What information should be collected?

765. Have Azure Databricks project management standards and procedures been identified / established and documented?

766. Are updated Azure Databricks project time & resource estimates reasonable based on the current Azure Databricks project stage?

767. Were the budget estimates reasonable?

768. Are the appropriate IT resources adequate to meet planned commitments?

769. Does the Azure Databricks project have a formal Azure Databricks project Charter?

770. Are best practices and metrics employed to identify issues, progress, performance, etc.?

771. Does the system design reflect the requirements?

772. Are Azure Databricks project logs kept up to date?

2.39 Change Management Plan: Azure Databricks

773. What provokes organizational change?

774. What are the current methods of sharing information and do there need to be new ones developed?

775. Will you need new processes?

776. What policies and procedures need to be changed?

777. How many people are required in each of the roles?

778. What is the negative impact of communicating too soon or too late?

779. What new roles are needed?

780. What did the people around you say about it?

781. What is the reason for the communication?

782. Is there for this application(s) and are the details available for distribution?

783. How prevalent is Resistance to Change?

784. How does the principle of senders and receivers make the Azure Databricks project communications effort more complex?

785. Why is it important?

786. What are the essentials of the message?

787. Has the training provider been established?

788. Has a training need analysis been carried out?

789. Who might present the most resistance?

790. What would be an estimate of the total cost for the activities required to carry out the change initiative?

791. Who will do the training?

792. Readiness -what is a successful end state?

3.0 Executing Process Group: Azure Databricks

793. Is activity definition the first process involved in Azure Databricks project time management?

794. How could stakeholders negatively impact your Azure Databricks project?

795. What are the critical steps involved in selecting measures and initiatives?

796. Do your results resemble a normal distribution?

797. Who will provide training?

798. What are the main parts of the scope statement?

799. Will outside resources be needed to help?

800. Measurable - are the targets measurable?

801. Does the case present a realistic scenario?

802. Mitigate. what will you do to minimize the impact should a risk event occur?

803. Are the necessary foundations in place to ensure the sustainability of the results of the programme?

804. When do you share the scorecard with managers?

805. What are the Azure Databricks project management deliverables of each process group?

806. What is the difference between using brainstorming and the Delphi technique for risk identification?

807. What are crucial elements of successful Azure Databricks project plan execution?

808. Why should Azure Databricks project managers strive to make jobs look easy?

809. What Azure Databricks projects and services are in the portfolio of your organization?

810. How can you use Microsoft Azure Databricks project and Excel to assist in Azure Databricks project risk management?

3.1 Team Member Status Report: Azure Databricks

811. What is to be done?

812. How does this product, good, or service meet the needs of the Azure Databricks project and your organization as a whole?

813. Are your organizations Azure Databricks projects more successful over time?

814. How can you make it practical?

815. When a teams productivity and success depend on collaboration and the efficient flow of information, what generally fails them?

816. Does the product, good, or service already exist within your organization?

817. How it is to be done?

818. Why is it to be done?

819. What specific interest groups do you have in place?

820. Do you have an Enterprise Azure Databricks project Management Office (EPMO)?

821. Are the products of your organizations Azure Databricks projects meeting customers objectives?

822. Does every department have to have a Azure Databricks project Manager on staff?

823. Are the attitudes of staff regarding Azure Databricks project work improving?

824. Will the staff do training or is that done by a third party?

825. Is there evidence that staff is taking a more professional approach toward management of your organizations Azure Databricks projects?

826. How much risk is involved?

827. The problem with Reward & Recognition Programs is that the truly deserving people all too often get left out. How can you make it practical?

828. Does your organization have the means (staff, money, contract, etc.) to produce or to acquire the product, good, or service?

829. How will resource planning be done?

3.2 Change Request: Azure Databricks

830. Who has responsibility for approving and ranking changes?

831. Will all change requests and current status be logged?

832. When do you create a change request?

833. How many times must the change be modified or presented to the change control board before it is approved?

834. What is a Change Request Form?

835. What are the basic mechanics of the Change Advisory Board (CAB)?

836. What are the requirements for urgent changes?

837. How does your organization control changes before and after software is released to a customer?

838. Who can suggest changes?

839. What are the Impacts to your organization?

840. Who needs to approve change requests?

841. What has an inspector to inspect and to check?

842. Has a formal technical review been conducted to assess technical correctness?

843. Who is included in the change control team?

844. Should staff call into the helpdesk or go to the website?

845. Can static requirements change attributes like the size of the change be used to predict reliability in execution?

846. Will the change use memory to the extent that other functions will be not have sufficient memory to operate effectively?

847. Screen shots or attachments included in a Change Request?

848. Have all related configuration items been properly updated?

849. How can you ensure that changes have been made properly?

3.3 Change Log: Azure Databricks

850. Is this a mandatory replacement?

851. Do the described changes impact on the integrity or security of the system?

852. Is the requested change request a result of changes in other Azure Databricks project(s)?

853. How does this change affect the timeline of the schedule?

854. When was the request submitted?

855. Who initiated the change request?

856. How does this relate to the standards developed for specific business processes?

857. Is the submitted change a new change or a modification of a previously approved change?

858. Where do changes come from?

859. How does this change affect scope?

860. Does the suggested change request seem to represent a necessary enhancement to the product?

861. Does the suggested change request represent a desired enhancement to the products functionality?

862. Is the change request open, closed or pending?

863. When was the request approved?

864. Is the change backward compatible without limitations?

865. Will the Azure Databricks project fail if the change request is not executed?

866. Is the change request within Azure Databricks project scope?

3.4 Decision Log: Azure Databricks

867. Which variables make a critical difference?

868. What was the rationale for the decision?

869. What alternatives/risks were considered?

870. Who will be given a copy of this document and where will it be kept?

871. How effective is maintaining the log at facilitating organizational learning?

872. It becomes critical to track and periodically revisit both operational effectiveness; Are you noticing all that you need to, and are you interpreting what you see effectively?

873. With whom was the decision shared or considered?

874. At what point in time does loss become unacceptable?

875. What makes you different or better than others companies selling the same thing?

876. Does anything need to be adjusted?

877. How do you know when you are achieving it?

878. How do you define success?

879. Who is the decisionmaker?

880. Is your opponent open to a non-traditional workflow, or will it likely challenge anything you do?

881. What is the average size of your matters in an applicable measurement?

882. How consolidated and comprehensive a story can you tell by capturing currently available incident data in a central location and through a log of key decisions during an incident?

883. Adversarial environment. is your opponent open to a non-traditional workflow, or will it likely challenge anything you do?

884. Linked to original objective?

885. Do strategies and tactics aimed at less than full control reduce the costs of management or simply shift the cost burden?

886. What is the line where eDiscovery ends and document review begins?

3.5 Quality Audit: Azure Databricks

887. How does your organization know that its relationships with other relevant organizations are appropriately effective and constructive?

888. What review processes are in place for your organizations major activities?

889. Is refuse and garbage adequately stored and disposed of with sufficient frequency to prevent contamination?

890. How does your organization ensure that equipment is appropriately maintained and producing valid results?

891. What are the main things that hinder your ability to do a good job?

892. How does your organization know that its system for commercializing research outputs is appropriately effective and constructive?

893. What is the collective experience of the team to be assigned to an audit?

894. How does your organization know that its system for ing staff research capability is appropriately effective and constructive?

895. How does your organization know that its system for attending to the particular needs of its international staff is appropriately effective and constructive?

896. Does the audit organization have experience in performing the required work for entities of your type and size?

897. Quality is about improvement and ability. The immediate questions that arise out of that statement are: (i) improvement on what, and (ii) able to whom?

898. How well do you think your organization engages with the outside community?

899. How does your organization know that its Governance system is appropriately effective and constructive?

900. How does your organization know that its staff financial services are appropriately effective and constructive?

901. How does your organization know that its system for inducting new staff to maximize workplace contributions are appropriately effective and constructive?

902. How does your organization know that its range of activities are being reviewed as rigorously and constructively as they could be?

903. Are measuring and test equipment that have been placed out of service suitably identified and excluded from use in any device reconditioning operation?

904. Is the reports overall tone appropriate?

905. How does your organization know that it is appropriately effective and constructive in preparing its staff for organizational aspirations?

906. Are complaint files maintained?

3.6 Team Directory: Azure Databricks

907. Why is the work necessary?

908. Timing: when do the effects of communication take place?

909. Process decisions: is work progressing on schedule and per contract requirements?

910. Process decisions: are contractors adequately prosecuting the work?

911. Where will the product be used and/or delivered or built when appropriate?

912. Process decisions: do job conditions warrant additional actions to collect job information and document on-site activity?

913. Decisions: is the most suitable form of contract being used?

914. What are you going to deliver or accomplish?

915. How and in what format should information be presented?

916. How does the team resolve conflicts and ensure tasks are completed?

917. How do unidentified risks impact the outcome of the Azure Databricks project?

918. Do purchase specifications and configurations match requirements?

919. Who is the Sponsor?

920. Who are your stakeholders (customers, sponsors, end s, team )?

921. Contract requirements complied with?

922. Decisions: what could be done better to improve the quality of the constructed product?

923. Process decisions: are all start-up, turn over and close out requirements of the contract satisfied?

924. Have you decided when to celebrate the Azure Databricks projects

completion date?

925. Who will report Azure Databricks project status to all stakeholders?

926. Days from the time the issue is identified?

3.7 Team Operating Agreement: Azure Databricks

927. What is the anticipated procedure (recruitment, solicitation of volunteers, or assignment) for selecting team ?

928. What istrative s will be put in place to the team and the teams supervisor?

929. Must your team rely on the expertise of other to complete tasks?

930. What is the number of cases currently teamed?

931. Do team need to frequently communicate as a full group to make timely decisions?

932. What went well?

933. Did you delegate tasks such as taking meeting minutes, presenting a topic and soliciting input?

934. Did you recap the meeting purpose, time, and expectations?

935. Are there influences outside the team that may affect performance, and if so, have you identified and addressed them?

936. To whom do you deliver your services?

937. Did you prepare participants for the next meeting?

938. Do you vary your voice pace, tone and pitch to engage participants and gain involvement?

939. Have you set the goals and objectives of the team?

940. What are the current caseload numbers in the unit?

941. What are the safety issues/risks that need to be addressed and/or that the team needs to consider?

942. Reimbursements: how will the team be reimbursed for expenses and time commitments?

943. How will you divide work equitably?

944. Do you record meetings for the already stated unable to attend?

945. What types of accommodations will be formulated and put in place for sustaining the team?

3.8 Team Performance Assessment: Azure Databricks

946. Do you promptly inform about major developments that may affect them?

947. If you have criticized someones work for method variance in your role as reviewer, what was the circumstance?

948. To what degree does the teams work approach provide opportunity for to engage in open interaction?

949. Lack of method variance in self-reported affect and perceptions at work: Reality or artifact?

950. What makes opportunities more or less obvious?

951. To what degree do understand and articulate the same purpose without relying on ambiguous abstractions?

952. To what degree will the team ensure that all equitably share the work essential to the success of the team?

953. To what degree does the teams purpose constitute a broader, deeper aspiration than just accomplishing short-term goals?

954. To what degree does the teams work approach provide opportunity for to engage in fact-based problem solving?

955. To what degree does the teams purpose contain themes that are particularly meaningful and memorable?

956. To what degree is the team cognizant of small wins to be celebrated along the way?

957. What are teams?

958. To what degree are staff involved as partners in the improvement process?

959. How do you manage human resources?

960. To what degree do all feel responsible for all agreed-upon measures?

961. To what degree is there a sense that only the team can succeed?

962. To what degree are sub-teams possible or necessary?

963. To what degree will the approach capitalize on and enhance the skills of all team in a manner that takes into consideration other demands on of the team?

964. To what degree can team vigorously define the teams purpose in considerations with others who are not part of the functioning team?

965. What structural changes have you made or are you preparing to make?

3.9 Team Member Performance Assessment: Azure Databricks

966. Are assessment validation activities performed?

967. To what degree are the goals realistic?

968. How often are assessments to be conducted?

969. What steps have you taken to improve performance?

970. What qualities does a successful Team leader possess?

971. Does adaptive training work?

972. How is performance assessment used in making future award decisions including options and extend/compete decisions?

973. What is collaboration?

974. How does your team work together?

975. What is the Business Management Oversight Process?

976. How are evaluation results utilized?

977. What is used as a basis for instructional decisions?

978. What is a general description of the processes under performance measurement and assessment?

979. What makes them effective?

980. Are any governance changes sufficient to impact achievement?

981. What are the key duties or tasks of the Ratee?

982. Is it critical or vital to the job?

983. What evaluation results did you have?

3.10 Issue Log: Azure Databricks

984. What is the status of the issue?

985. Who reported the issue?

986. What is the impact on the Business Case?

987. Who have you worked with in past, similar initiatives?

988. Is the issue log kept in a safe place?

989. What would have to change?

990. Why not more evaluators?

991. What steps can you take for positive relationships?

992. Who is involved as you identify stakeholders?

993. Can you think of other people who might have concerns or interests?

994. Who do you turn to if you have questions?

995. How were past initiatives successful?

996. Is access to the Issue Log controlled?

997. What approaches do you use?

998. Why do you manage communications?

4.0 Monitoring and Controlling Process Group: Azure Databricks

999. If action is called for, what form should it take?

1000. Who needs to be involved in the planning?

1001. Are the services being delivered?

1002. Where is the Risk in the Azure Databricks project?

1003. How were collaborations developed, and how are they sustained?

1004. Who needs to be engaged upfront to ensure use of results?

1005. Is the verbiage used appropriate and understandable?

1006. How many potential communications channels exist on the Azure Databricks project?

1007. How will staff learn how to use the deliverables?

1008. How should needs be met?

1009. Is the program making progress in helping to achieve the set results?

1010. What resources (both financial and non-financial) are available/needed?

1011. Is there undesirable impact on staff or resources?

1012. What is the timeline for the Azure Databricks project?

1013. What areas does the group agree are the biggest success on the Azure Databricks project?

1014. How to ensure validity, quality and consistency?

1015. What resources are necessary?

1016. Are there areas that need improvement?

4.1 Project Performance Report: Azure Databricks

1017. What is the PRS?

1018. Next Steps?

1019. To what degree do team feel that the purpose of the team is important, if not exciting?

1020. To what degree is the information network consistent with the structure of the formal organization?

1021. To what degree are the clear on what they are individually responsible for and what they are tly responsible for?

1022. How can Azure Databricks project sustainability be maintained?

1023. To what degree will each member have the opportunity to advance his or her professional skills in all three of the above categories while contributing to the accomplishment of the teams purpose and goals?

1024. To what degree can the cognitive capacity of individuals accommodate the

flow of information?

1025. To what degree are the demands of the task compatible with and converge with the relationships of the informal organization?

1026. To what degree does the team possess adequate hip to achieve its ends?

1027. To what degree are the skill areas critical to team performance present?

1028. How will procurement be coordinated with other Azure Databricks project aspects, such as scheduling and performance reporting?

1029. To what degree are the goals ambitious?

1030. What is the degree to which rules govern information exchange between individuals within your organization?

4.2 Variance Analysis: Azure Databricks

1031. How do you authorization to proceed with all authorized work?

1032. Does the contractors system include procedures for measuring the performance of critical subcontractors?

1033. Did a new competitor enter the market?

1034. Contemplated overhead expenditure for each period based on the best information currently is available?

1035. How are material, labor, and overhead variances calculated and recorded?

1036. Are records maintained to show how management reserves are used?

1037. Favorable or unfavorable variance?

1038. What does an unfavorable overhead volume variance mean?

1039. How do you manage changes in the nature of the overhead requirements?

1040. What is the actual cost of work performed?

1041. What business event caused the fluctuation?

1042. Are all elements of indirect expense identified to overhead cost budgets of Azure Databricks projections?

1043. What is the expected future profitability of each customer?

1044. When, during the last four quarters, did a primary business event occur causing a fluctuation?

1045. Why are standard cost systems used?

1046. Are there knowledgeable Azure Databricks projections of future performance?

4.3 Earned Value Status: Azure Databricks

1047. How much is it going to cost by the finish?

1048. Verification is a process of ensuring that the developed system satisfies the stakeholders agreements and specifications; Are you building the product right? What do you ?

1049. How does this compare with other Azure Databricks projects?

1050. Are you hitting your Azure Databricks projects targets?

1051. Where is evidence-based earned value in your organization reported?

1052. If earned value management (EVM) is so good in determining the true status of a Azure Databricks project and Azure Databricks project its completion, why is it that hardly any one uses it in information systems related Azure Databricks projects?

1053. Earned value can be used in almost any Azure Databricks project situation and in almost any Azure Databricks project environment. it may be used on large Azure Databricks projects, medium sized Azure Databricks projects, tiny Azure Databricks projects (in cut-down form), complex and simple Azure Databricks projects and in any market sector. some people, of course, know all about earned

value, they have used it for years - but perhaps not as effectively as they could have?

1054. Where are your problem areas?

1055. What is the unit of forecast value?

1056. When is it going to finish?

1057. Validation is a process of ensuring that the developed system will actually achieve the stakeholders desired outcomes; Are you building the right product? What do you validate?

4.4 Risk Audit: Azure Databricks

1058. What expertise does the Board have on quality, outcomes, and errors?

1059. Extending the consideration on the halo effect, to what extent are auditors able to build skepticism in evidence review?

1060. Do you have proper induction processes for all new paid staff and volunteers who have a specific role and responsibility?

1061. Does your organization have any policies or procedures to guide its decision-making (code of conduct for the board, conflict of interest policy, etc.)?

1062. What is the effect of globalisation; is business becoming too complex and can the auditor rely on auditing standards?

1063. Does willful intent modify risk-based auditing?

1064. What is the anticipated volatility of the requirements?

1065. Do you have position descriptions for all office bearers/staff?

1066. What are the costs associated with late delivery or a defective product?

1067. Is safety information provided to all involved?

1068. Will safety checks of personal equipment supplied by competitors be conducted?

1069. What limitations do auditors face in effectively applying risk-assessment results to the risk of material misstatement measures?

1070. What can you do to manage outcomes?

1071. Are tools for analysis and design available?

1072. Are duties out-of-class?

1073. What are the strategic implications with clients when auditors focus audit resources based on business-level risks?

1074. What are the risks that could stop you from achieving your objectives?

1075. Does your board meet regularly and document all decisions and actions?

1076. Have top software and customer managers formally committed to the Azure Databricks project?

4.5 Contractor Status Report: Azure Databricks

1077. How does the proposed individual meet each requirement?

1078. What was the budget or estimated cost for your organizations services?

1079. What are the minimum and optimal bandwidth requirements for the proposed solution?

1080. Who can list a Azure Databricks project as organization experience, your organization or a previous employee of your organization?

1081. What was the overall budget or estimated cost?

1082. How is risk transferred?

1083. What was the final actual cost?

1084. Describe how often regular updates are made to the proposed solution. Are corresponding regular updates included in the standard maintenance plan?

1085. Are there contractual transfer concerns?

1086. What is the average response time for answering a call?

1087. How long have you been using the services?

1088. What was the actual budget or estimated cost for your organizations services?

1089. What process manages the contracts?

1090. If applicable; describe your standard schedule for new software version releases. Are new software version releases included in the standard maintenance plan?

4.6 Formal Acceptance: Azure Databricks

1091. What lessons were learned about your Azure Databricks project management methodology?

1092. What function(s) does it fill or meet?

1093. Was business value realized?

1094. Was the sponsor/customer satisfied?

1095. General estimate of the costs and times to complete the Azure Databricks project?

1096. How well did the team follow the methodology?

1097. Does it do what client said it would?

1098. Do you buy pre-configured systems or build your own configuration?

1099. Who would use it?

1100. How does your team plan to obtain formal acceptance on your Azure Databricks project?

1101. Was the Azure Databricks project goal achieved?

1102. What can you do better next time?

1103. What are the requirements against which to test, Who will execute?

1104. Did the Azure Databricks project achieve its MOV?

1105. Was the Azure Databricks project managed well?

1106. Do you buy-in installation services?

1107. Was the client satisfied with the Azure Databricks project results?

1108. Have all comments been addressed?

1109. Did the Azure Databricks project manager and team act in a professional and ethical manner?

1110. What is the Acceptance Management Process?

5.0 Closing Process Group: Azure Databricks

1111. What were things that you need to improve?

1112. What were the desired outcomes?

1113. Who are the Azure Databricks project stakeholders?

1114. What were things that you did very well and want to do the same again on the next Azure Databricks project?

1115. What is the Azure Databricks project Management Process?

1116. What were things that you did well, and could improve, and how?

1117. What level of risk does the proposed budget represent to the Azure Databricks project?

1118. What were the actual outcomes?

1119. When will the Azure Databricks project be done?

1120. Just how important is your work to the overall success of the Azure Databricks project?

1121. How well did the team follow the chosen processes?

1122. Was the schedule met?

1123. Did the Azure Databricks project team have enough people to execute the Azure Databricks project plan?

1124. Did the Azure Databricks project team have the right skills?

1125. Is the Azure Databricks project funded?

1126. Is this a follow-on to a previous Azure Databricks project?

5.1 Procurement Audit: Azure Databricks

1127. What are the threats to supplier relations?

1128. Are requisitions and other purchase requests batched to reduce the number of orders issued?

1129. Was confidentiality ensured when necessary?

1130. Are eu procurement regulations applicable?

1131. In open and restricted procedures, did the contracting authority make sure that there is no substantive change to the bid due to this clearing process?

1132. Are the journals and ledgers kept current for all funds?

1133. Did your organization permit tenderers to submit variants, thus offering space for creative solutions and added value?

1134. Are all claims certified by the officer giving rise to the claim (usually the purchasing agent)?

1135. Did the contracting authority compliance with the basic requirements of the competition?

1136. Are there policies regarding special approval for capital expenditures?

1137. Was the decision on the award process accurate and adequately communicated?

1138. Were results of the award procedures published?

1139. Are trial balances taken weekly for general ledgers for all funds?

1140. Are tenders who do not comply with the requirements specified in the request for tenders rejected?

1141. Was invitation to tender to each specific contract issued after the evaluation of the indicative tenders was completed?

1142. Where applicable, did your organization adequately manage experts employed to assist in the procurement process?

1143. Did the chosen procedure ensure competition and transparency?

1144. Are advance payments to employees properly authorized and controlled?

1145. Are the right skills, experiences and competencies present in the acquisition workgroup and are the necessary outside specialists involved in part of the process?

1146. Are transportation charges verified?

5.2 Contract Close-Out: Azure Databricks

1147. Have all contract records been included in the Azure Databricks project archives?

1148. Have all contracts been completed?

1149. Have all acceptance criteria been met prior to final payment to contractors?

1150. What happens to the recipient of services?

1151. Has each contract been audited to acceptance and delivery?

1152. Change in circumstances?

1153. How does it work?

1154. Change in attitude or behavior?

1155. How/when used ?

1156. Was the contract type appropriate?

1157. What is capture management?

1158. Have all contracts been closed?

1159. How is the contracting office notified of the automatic contract close-out?

1160. Parties: who is involved?

1161. Was the contract complete without requiring numerous changes and revisions?

1162. Was the contract sufficiently clear so as not to result in numerous disputes and misunderstandings?

1163. Are the signers the authorized officials?

1164. Parties: Authorized?

1165. Change in knowledge?

5.3 Project or Phase Close-Out: Azure Databricks

1166. Does the lesson educate others to improve performance?

1167. What are the mandatory communication needs for each stakeholder?

1168. Who are the Azure Databricks project stakeholders and what are roles and involvement?

1169. What are the marketing communication needs for each stakeholder?

1170. Did the Azure Databricks project management methodology work?

1171. What was expected from each stakeholder?

1172. Planned remaining costs?

1173. What were the goals and objectives of the communications strategy for the Azure Databricks project?

1174. Who controlled the resources for the Azure Databricks project?

1175. What is a Risk Management Process?

1176. What advantages do the an individual interview have over a group meeting, and vice-versa?

1177. Were risks identified and mitigated?

1178. In addition to assessing whether the Azure Databricks project was successful, it is equally critical to analyze why it was or was not fully successful. Are you including this?

1179. In preparing the Lessons Learned report, should it reflect a consensus viewpoint, or should the report reflect the different individual viewpoints?

1180. What was the preferred delivery mechanism?

1181. What stakeholder group needs, expectations, and interests are being met by the Azure Databricks project?

1182. What information did each stakeholder need to contribute to the Azure Databricks projects success?

1183. Does the lesson describe a function that would be done differently the next time?

5.4 Lessons Learned: Azure Databricks

1184. Did the Azure Databricks project management methodology work?

1185. What skills did you need that were missing on this Azure Databricks project?

1186. Would you spend your own money to fix this issue?

1187. What is the growth stage of the organization?

1188. How extensive is middle management?

1189. Who managed most of the communication within the Azure Databricks project?

1190. Is the lesson based on actual Azure Databricks project experience rather than on independent research?

1191. Who needs to learn lessons?

1192. How well do you feel the executives ed this Azure Databricks project?

1193. What is in the future?

1194. How adequately involved did you feel in Azure Databricks project decisions?

1195. What regulatory regime controlled how your organization head and program manager directed your organization and Azure Databricks project?

1196. What would you like to see better documented about how to use existing processes on this type of Azure Databricks project?

1197. How efficient and effective were meetings?

1198. How efficient were Azure Databricks project team meetings conducted?

1199. What rewards do the individuals seek?

1200. How comprehensive was integration testing?

1201. Overall, how effective were the efforts to prepare you and your

organization for the impact of the product/service of the Azure Databricks project?

1202. What are the influence patterns?

Index

(Index page number references Only of use in Print Version)