Domain III · Identify Data Needs · 26% of the exam
III.7 Oversee data evaluation
Task 7 of Identify Data Needs
May 9: Low Sept 6: not tested 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.
- Assess data quality dimensions including accuracy, completeness, and consistency
- Analyze data distributions and identify potential biases or gaps
- Evaluate data freshness and relevance for AI model training
- Review data schema and structure for modeling compatibility
- Conduct exploratory data analysis to understand data characteristics
The rule
Evaluation describes distributions, quality dimensions, gaps and bias. PMI's data quality management good practices include investing in automation to reduce error.
Study Hall misses on this task (1)
- Full-length Q5 · confidence High · 31sScenario: Inconsistent outputs during a data quality review; NEXT (Module 02 data quality management practices)Decided by: PMI's listed good practice: invest in automation to reduce errorChose: Standardised rules and policies (a governance practice)
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What to read
- Workbook p. 53-56 — Describe Data; Explore Data and Cognitive Data Requirements; Verify Data Quality
- Module 02 Lessons 12-14: Data Quantity and Quality Issues; Lesson 35: What Is Data Quality Management?
- Leading and Managing AI Projects p. 23 — In Practice: Evaluating Data Readiness for AI
- Study guide Lesson 33: ECO Task III.7 — Oversee Data Evaluation