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_____ = Total points for this section

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

      Transfer your score to the Hardware accelerators for machine learning Index at the beginning of the Self-Assessment.

      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. Are all requirements met?

      <--- Score

      2. What are the Hardware accelerators for machine learning tasks and definitions?

      <--- Score

      3. What is the scope?

      <--- Score

      4. Is data collected and displayed to better understand customer(s) critical needs and requirements.

      <--- Score

      5. Is it clearly defined in and to your organization what you do?

      <--- Score

      6. How do you build the right business case?

      <--- Score

      7. What is a worst-case scenario for losses?

      <--- Score

      8. Do you all define Hardware accelerators for machine learning in the same way?

      <--- Score

      9. Has the improvement team collected the ‘voice of the customer’ (obtained feedback – qualitative and quantitative)?

      <--- Score

      10. Who approved the Hardware accelerators for machine learning scope?

      <--- Score

      11. Have all of the relationships been defined properly?

      <--- Score

      12. How are consistent Hardware accelerators for machine learning definitions important?

      <--- Score

      13. What Hardware accelerators for machine learning services do you require?

      <--- Score

      14. Do you have organizational privacy requirements?

      <--- Score

      15. When are meeting minutes sent out? Who is on the distribution list?

      <--- Score

      16. Are approval levels defined for contracts and supplements to contracts?

      <--- Score

      17. How do you manage unclear Hardware accelerators for machine learning requirements?

      <--- Score

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

      <--- Score

      19. What baselines are required to be defined and managed?

      <--- Score

      20. What is the definition of Hardware accelerators for machine learning excellence?

      <--- Score

      21. Who are the Hardware accelerators for machine learning improvement team members, including Management Leads and Coaches?

      <--- Score

      22. What is out-of-scope initially?

      <--- Score

      23. Is Hardware accelerators for machine learning linked to key stakeholder goals and objectives?

      <--- Score

      24. What happens if Hardware accelerators for machine learning’s scope changes?

      <--- Score

      25. What system do you use for gathering Hardware accelerators for machine learning information?

      <--- Score

      26. What is the worst case scenario?

      <--- Score

      27. How do you catch Hardware accelerators for machine learning definition inconsistencies?

      <--- Score

      28. Has your scope been defined?

      <--- Score

      29. Have specific policy objectives been defined?

      <--- Score

      30. What are the Hardware accelerators for machine learning use cases?

      <--- Score

      31. Are audit criteria, scope, frequency and methods defined?

      <--- Score

      32. Do you have a Hardware accelerators for machine learning success story or case study ready to tell and share?

      <--- Score

      33. Is Hardware accelerators for machine learning required?

      <--- Score

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

      <--- Score

      35. What is the scope of the Hardware accelerators for machine learning work?

      <--- Score

      36. In what way can you redefine the criteria of choice clients have in your category in your favor?

      <--- Score

      37. How will variation in the actual durations of each activity be dealt with to ensure that the expected Hardware accelerators for machine learning results are met?

      <--- Score

      38. How can the value of Hardware accelerators for machine learning be defined?

      <--- Score

      39. Has a team charter been developed and communicated?

      <--- Score

      40. What are (control) requirements for Hardware accelerators for machine learning Information?

      <--- Score

      41. Why are you doing Hardware accelerators for machine learning and what is the scope?

      <--- Score

      42. How do you manage scope?

      <--- Score

      43. What customer feedback methods were used to solicit their input?

      <--- Score

      44. How do you think the partners involved in Hardware accelerators for machine learning would have defined success?

      <--- Score

      45. Does the team have regular meetings?

      <--- Score

      46. What is in scope?

      <--- Score

      47. How does the Hardware accelerators for machine learning manager ensure against scope creep?

      <--- Score

      48. Where can you gather more information?

      <--- Score

      49. What sort of initial information to gather?

      <--- Score

      50. What key stakeholder process output measure(s) does Hardware accelerators

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