AI Associate Data for AI Practice Question
Data quality is critical for AI model performance. Which three data quality dimensions should be monitored? (Choose three.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Completeness
Completeness, timeliness, and consistency are fundamental data quality dimensions. Volume is not a quality dimension; uniqueness is related to consistency but not always required.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Completeness
Why this is correct
Ensures no missing values that could bias the model.
- ✓
Consistency
Why this is correct
Data should be consistent across sources to avoid conflicting signals.
- ✗
Uniqueness
Why it's wrong here
While important for certain contexts, uniqueness is not a core quality dimension typically required for all AI models.
- ✓
Timeliness
Why this is correct
Data must be current to reflect reality, especially for real-time AI.
- ✗
Volume
Why it's wrong here
Volume refers to quantity, not quality; a quality dimension evaluates characteristics.
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