Domain III · Identify Data Needs · 26% of the exam
III.1 Define required data
Task 1 of Identify Data Needs
May 9: Mid Sept 6: Mid 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.
- Specify data types and formats needed for AI model training
- Determine data volume requirements and sampling strategies
- Identify temporal and granularity requirements for data collection
- Define data quality standards and acceptance criteria
- Map data requirements to business objectives and use cases
The rule
Sampling rate, history depth, granularity and volume are III.1's own enablers. Answer with the data requirement, not with infrastructure.
Study Hall misses on this task (1)
- Mini 2 #6 · confidence High · 34sScenario: How often to sample sensors and how far back to collectDecided by: The stem restates III.1's enabler on temporal and granularity requirementsChose: Deployment infrastructure requirements
Logged misses on this task
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What to read
- Workbook p. 54, 57-58 — Explore Data and Cognitive Data Requirements; Training and Test Data Requirements
- Module 02 Lessons 17-23: Identifying Data Sets; What Is Training Data?; Does AI Need a Lot of Data?; What Is Ground Truth Data?
- Leading and Managing AI Projects p. 21 — Key Activities in Phase II for Data Understanding
- Study guide Lesson 1: ECO Task III.1 — Define Required Data
Glossary terms in the enablers: data collection