Domain IV · Manage AI Model Development and Evaluation · 16% of the exam
IV.5 Verify data quality for go/no-go decision to conduct data preparation
Task 5 of Manage AI Model Development and Evaluation
May 9: Mid Sept 6: Low Study Hall misses: 1 Logged: 0
Enablers (verbatim, ECO)
PMI, ECO p. 5: enablers are "illustrative examples of the work associated with the task" and "not meant to be an exhaustive list but rather offer a few examples to help demonstrate what the task encompasses."
Ours: PMI's stems paraphrase one of these as a symptom; the right option is that enabler in PMI's words; the distractors are its siblings or a neighbouring task's enablers.
- Conduct final data quality assessments before model training
- Validate data preprocessing and transformation results
- Assess data representativeness and potential bias issues
- Make decisions on data readiness for model development
- Document data quality findings and recommendations
The rule
Defects found before preparation are what preparation fixes. The data-quality go/no-go comes after preparation, not before it.
Study Hall misses on this task (1)
- Practice IV #15 · confidence High · 19sScenario: Data-quality defects found before data preparation beginsDecided by: Preparation is the phase that fixes these; proceed, the gate comes after prepChose: Calling a no-go now
Logged misses on this task
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What to read
- Workbook p. 55-56 — Verify Data Quality
- Module 03 Lesson 44: CPMAI Phase III Ensuring Readiness; Lesson 50: Phase III Go or No-Go; Lesson 51: When to Iterate Back
- Leading and Managing AI Projects p. 27 — Transition to Model Development
- Study guide Lesson 7: ECO Task IV.5 — Verify data quality for go/no-go decision to conduct data preparation