DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is profiling a new dataset containing customer information. When assessing data quality, which metric would be most appropriate to determine if the 'email' column contains valid email addresses?
⚠ Common exam trap
Many exam-takers confuse data quality dimensions — candidates often pick cardinality or null count because they sound like 'profiling' metrics, but the question specifically asks about validating format, which only pattern analysis addresses.
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
✓
Pattern analysis
Pattern analysis is the correct metric because it validates data against a defined format or regular expression — exactly what's needed to confirm that values in the 'email' column conform to the structure of a valid email address (e.g., user@domain.tld). Data profiling tools use pattern/format analysis to detect values that deviate from expected structures, making it the appropriate quality dimension for format validation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pattern analysis
Why this is correct
Pattern analysis examines whether values conform to an expected format, such as the structure of a valid email address. Applying it to the 'email' column reveals entries that deviate from that pattern, directly satisfying the requirement to assess validity of email addresses.
- ✗
Null count
Why it's wrong here
Null count only tallies missing entries, so a column full of malformed addresses would still pass. It is the right metric for completeness checks, such as deciding whether a field is populated enough to use.
- ✗
Cardinality
Why it's wrong here
Cardinality counts distinct values, so it cannot detect whether each address matches a valid email pattern — a column of entirely unique but malformed strings would score perfectly. It is tempting because cardinality genuinely suits assessing uniqueness, duplication or identifier suitability, such as confirming a customer ID column holds no repeated values.
- ✗
Row count
Why it's wrong here
Row count tallies records, not the format of values within the email column, so it cannot reveal whether addresses are valid. It is tempting because row count is a legitimate completeness metric, and would be the correct choice when verifying that no customer records were dropped during an import.
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JA
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.