DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is profiling a new dataset and needs to assess data quality. Which two metrics are most appropriate for evaluating the completeness and consistency of the data? (Choose two.)
⚠ Common exam trap
The trap here is selecting metrics that seem related to data quality but do not directly measure completeness or consistency, such as distinct value counts or record length.
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
✓
Frequency of values violating defined business rules
Completeness is assessed by the percentage of missing values, which shows how much data is absent. Consistency is assessed by the frequency of business rule violations, which reveals non-conforming data. Together, these metrics provide a clear picture of data quality issues that need addressing before analysis.
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 type mismatch count between source and target
Why it's wrong here
Data type mismatch count is relevant during ETL to ensure correct loading, but it is not a standard data quality metric for completeness or consistency. It is more about schema validation. Completeness focuses on missing values, while consistency focuses on uniformity across records.
- ✓
Frequency of values violating defined business rules
Why this is correct
The frequency of values violating business rules measures consistency and validity. Business rules define acceptable ranges or formats, and violations indicate data that does not conform. This metric is crucial for identifying data quality issues that could lead to incorrect analysis or decisions.
- ✗
Number of distinct values in a column
Why it's wrong here
The number of distinct values measures cardinality, which is useful for understanding data distribution and potential key candidates, but it does not directly assess completeness or consistency. It can reveal inconsistencies if unexpected values appear, but it is not a primary metric for those dimensions.
- ✓
Percentage of missing values per column
Why this is correct
The percentage of missing values per column directly measures completeness. A high percentage indicates that the column lacks sufficient data, which can impair analysis. This metric helps identify columns that may need imputation or exclusion, making it essential for data quality assessment.
- ✗
Average record length in bytes
Why it's wrong here
Average record length is a storage metric, not a data quality metric. It does not provide insight into completeness or consistency. While it might indicate anomalies if records are truncated, it is not a standard measure for assessing data quality dimensions.
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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
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.