DA0-002 Data Analysis Practice Question
A data analyst is preparing a dataset for analysis and needs to ensure data quality. Which TWO of the following are dimensions of data quality?
⚠ Common exam trap
DA0-002 often mixes big-data Vs (volume, velocity, variety) with data quality dimensions (accuracy, consistency, completeness), so candidates must distinguish characteristics of data from measures of its quality.
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
✓
Consistency
Consistency (C) is a core dimension of data quality because it ensures that the same data values are represented uniformly across different datasets, systems, or records, preventing contradictions that would corrupt analysis results. Accuracy (E) is also a fundamental data quality dimension, as it verifies that data correctly reflects the real-world entities or events it is meant to describe, which is essential for trustworthy analysis. In contrast, Volume (A), Velocity (B), and Variety (D) are the three defining characteristics of big data (the '3 Vs'), describing the scale, speed, and diversity of data rather than its quality. Therefore, only Consistency and Accuracy belong to the set of data quality dimensions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Volume
Why it's wrong here
Volume describes the quantity of data stored or processed, a big data characteristic, not a measure of quality. It is tempting because the Vs framework is widely cited, but quality dimensions are accuracy, completeness, consistency, validity, timeliness and uniqueness.
- ✗
Velocity
Why it's wrong here
Velocity describes the speed at which data arrives and is processed, a big data characteristic rather than a quality dimension. It is tempting because the Vs framework is commonly taught, yet quality is assessed through accuracy, completeness, consistency, validity, timeliness and uniqueness.
- ✓
Consistency
Why this is correct
Consistency is a recognised data quality dimension, confirming that values remain uniform across systems and records without contradictory entries. It satisfies the stem's requirement for genuine quality dimensions, alongside accuracy, completeness, timeliness and validity, so it qualifies as one of the two correct selections.
- ✗
Variety
Why it's wrong here
Variety describes the range of data types and sources, a characteristic of big data, not a data quality dimension. It is tempting because the Vs framework is widely taught, but quality dimensions concern accuracy, completeness, consistency, validity, timeliness and uniqueness.
- ✓
Accuracy
Why this is correct
Accuracy is a core data quality dimension, confirming that values correctly reflect the real-world entities they describe. It satisfies the stem's requirement for genuine quality dimensions, alongside consistency, completeness and validity, so it qualifies as one of the two correct selections.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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