DA0-002 Data Concepts and Environments Practice Question
During an ETL process, a data quality check fails due to duplicate customer IDs. Which data quality dimension is violated?
⚠ Common exam trap
Candidates often confuse uniqueness with accuracy, thinking a duplicate ID is 'inaccurate' data, but accuracy concerns correctness of values, not their distinctness.
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
✓
Uniqueness
Duplicate customer IDs violate the uniqueness dimension because uniqueness ensures that each record in a dataset has a distinct identifier with no duplicates. In an ETL process, a primary key or unique constraint on the customer ID column would reject duplicate values, causing the data quality check to fail. This is distinct from consistency, which checks for logical agreement across data sources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Consistency
Why it's wrong here
Consistency is about uniform representation, not duplicates.
- ✓
Uniqueness
Why this is correct
Duplicates violate the uniqueness dimension.
- ✗
Completeness
Why it's wrong here
Completeness is about missing data, not duplicates.
- ✗
Accuracy
Why it's wrong here
Accuracy concerns correctness of values, not duplicates.
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