DA0-002 Data Acquisition and Preparation Practice Question
A data analyst at a retail company is profiling a newly acquired customer table. They observe that the 'last_purchase_date' column contains values such as '2023-13-45', '0000-00-00', and '2023-02-30'. Which data quality dimension is primarily violated?
⚠ Common exam trap
A common mix-up: candidates confuse malformed values with missing values, leading to a completeness answer instead of validity.
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
✓
Validity
The date values shown are not valid calendar dates, meaning they fail format and range checks. Validity ensures data adheres to defined rules, such as a date being a real date. Completeness, consistency, and uniqueness address different aspects and do not capture the core problem of impossible dates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Uniqueness
Why it's wrong here
Uniqueness ensures that no duplicate records exist for a given entity. The issue described is about invalid date values, not duplicates. Multiple customers could have the same last purchase date legitimately, so uniqueness is irrelevant to this data quality problem.
- ✗
Completeness
Why it's wrong here
Completeness refers to whether all required data is present, such as missing values or nulls. In this scenario, the dates are present but malformed, not missing. Therefore, completeness is not the primary issue; the values exist but are invalid according to date standards.
- ✓
Validity
Why this is correct
Validity checks whether data conforms to defined formats, types, or business rules. Here, the date values are not valid calendar dates (month 13, day 45, day 30 in February), so they violate the validity dimension. The analyst must apply date validation rules to flag or correct these entries before using the data for analysis.
- ✗
Consistency
Why it's wrong here
Consistency ensures that data is uniform across different sources or within a dataset. While inconsistent date formats could be a problem, the given values are not merely formatted differently; they are impossible dates. Thus, consistency is not the core violation here.
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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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