DA0-002 Data Acquisition and Preparation Practice Question
Exhibit
2023-08-15 14:32:10 ERROR: Data conversion failed for column 'salary' in row 45: value 'N/A' cannot be converted to numeric.
Refer to the exhibit. What data quality issue is indicated?
⚠ Common exam trap
It's easy for candidates to confuse non-standardized data entry (formatting variants of the same value) with data duplication (identical repeated records) — candidates often pick duplication because both involve 'the same thing appearing multiple times'.
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
✓
Non-standardized data entry
The exhibit shows the same logical value entered in multiple inconsistent formats (e.g., 'NY', 'New York', 'new york', 'N.Y.'), which is the hallmark of non-standardized data entry. The data is not duplicated (different spellings), not an outlier (no extreme numeric value), and not a referential inconsistency (no conflicting values across tables) — it is a formatting/standardization problem at the point of capture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data inconsistency
Why it's wrong here
Data inconsistency means the same attribute holds conflicting values across related records or systems; the exhibit shows a value outside the expected range, not a mismatch between sources. It is tempting because both involve wrong-looking data, and it would be correct when two systems disagree on the same field.
- ✓
Non-standardized data entry
Why this is correct
The exhibit shows the same values recorded in inconsistent formats and spellings, indicating non-standardised data entry. This inconsistency stems from missing input validation or controlled vocabularies at the point of capture rather than duplication or completeness problems.
- ✗
Outlier
Why it's wrong here
An outlier is a value distant from the rest of the distribution; the exhibit shows conflicting values across records rather than a single extreme point. It is tempting because outliers are a common quality issue, and it would be correct when one record sits far outside the expected range.
- ✗
Data duplication
Why it's wrong here
Duplication means identical records repeated, which the exhibit would show as matching rows rather than the inconsistent, missing or malformed values actually displayed. It is tempting because duplicate detection is a genuine data quality dimension, and deduplication would be the correct remediation when a dataset contains repeated customer or transaction records.
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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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