DA0-002 Data Analysis Practice Question
A dataset contains customer records with a column for 'Phone Number' that should be unique. However, the analyst finds several duplicate phone numbers. Which data quality dimension is primarily affected?
⚠ Common exam trap
The trap here is conflating uniqueness with accuracy or consistency — candidates see 'duplicate' and think 'wrong data,' but duplicates are a cardinality problem, not a correctness problem.
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
Uniqueness measures whether each real-world entity appears exactly once in the dataset. Since 'Phone Number' is intended to be a unique identifier per customer, finding duplicate values directly violates that expectation, so the affected dimension is uniqueness. Completeness, accuracy, and consistency describe different properties (missing values, correctness, and uniformity across sources) and are not what duplicate keys violate.
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 it's wrong here
Completeness measures whether required values are absent, not whether present values repeat; duplicate phone numbers are populated, so nothing is missing. It is tempting because duplicates can signal records that failed to merge, yet the dimension tested here is uniqueness, which the stem's duplicate detection directly targets.
- ✗
Accuracy
Why it's wrong here
Accuracy concerns whether a value correctly describes the real-world entity, whereas duplicated phone numbers may each be factually correct yet wrongly repeated across records. It is tempting because duplicates often accompany data-entry errors, but the stem specifies uniqueness as the violated expectation, pointing to a different dimension.
- ✓
Uniqueness
Why this is correct
Duplicate phone numbers violate the requirement that each value in the column be distinct, which is precisely what the uniqueness dimension measures. Completeness, accuracy and consistency concern missing, wrong or conflicting values, not repeated ones.
- ✗
Consistency
Why it's wrong here
Consistency compares the same attribute across systems or formats, not the repetition of values within one column. It is tempting because duplicate entries can arise from inconsistent source systems, but the stem describes a single dataset where a uniqueness expectation is breached, which is the dimension actually affected.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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