Question 208 of 986
Data Quality Dimensions
Which THREE data quality dimensions are commonly assessed in a data profiling task?
Quick Answer
The answer is completeness, consistency, and uniqueness. These three data quality dimensions are commonly assessed in a data profiling task because they directly measure whether data is usable and reliable for analysis. Completeness checks for missing values, consistency ensures data values follow the same representation rules across records—such as flagging when 'NY' and 'New York' appear in the same state column—and uniqueness verifies that no duplicate entries exist. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of core profiling objectives, often appearing in scenario-based items where you must identify which dimensions a profiling tool like Informatica or Talend would evaluate. A common trap is confusing accuracy with consistency; remember that consistency focuses on internal agreement, not correctness against a reference. A helpful memory tip is the acronym C-C-U: Completeness, Consistency, Uniqueness—the three pillars of initial data profiling.
⚠ Common exam trap
CompTIA often tests the distinction between data quality dimensions (completeness, consistency, uniqueness) and system-level attributes (scalability, availability), leading candidates to mistakenly select non-quality terms like 'Availability' or 'Scalability' because they sound relevant to data management.
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 is a core data quality dimension assessed in data profiling because it evaluates whether data values are free from contradiction and adhere to the same representation rules across records. In profiling tools like Informatica or Talend, consistency checks identify violations such as 'NY' vs 'New York' in a state column, ensuring semantic uniformity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scalability
Why it's wrong here
Scalability is a system performance metric, not a data quality dimension.
- ✓
Consistency
Why this is correct
Consistency ensures uniform data representation, a common profiling check.
- ✓
Uniqueness
Why this is correct
Uniqueness ensures no duplicate records, part of data profiling.
- ✗
Availability
Why it's wrong here
Availability is about system uptime, not data quality.
- ✓
Completeness
Why this is correct
Completeness checks for missing values, a standard profiling dimension.
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Same concept, more angles
1 more way this is tested on DA0-002
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Refer to the exhibit. Based on the data profiling results, what is a likely data quality issue?
hard- A.Completeness
- B.Accuracy
- ✓ C.Validity
- D.Consistency
Why C: The min of 0 and max of 150 are biologically implausible for age. A 0-year-old or 150-year-old customer likely indicates invalid data, affecting validity.
Last reviewed: Jun 30, 2026
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.
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