DA0-002 Data Concepts and Environments Practice Question
A data analyst receives a dataset with inconsistent date formats (e.g., "01/02/2023", "2023-01-02", "Jan 2, 2023"). Which data quality dimension is most directly affected?
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 refers to the uniformity of data representation. Inconsistent date formats violate consistency, not accuracy, completeness, or timeliness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy concerns whether values correctly represent reality; mixed date formats are still correct values, just represented differently. Accuracy would be the affected dimension if, say, a date were recorded as 2023-01-02 when the event occurred 2023-02-01.
- ✓
Consistency
Why this is correct
Consistency measures whether values conform to the same format and units across a dataset. Mixed representations of the same date attribute violate that uniformity, whereas accuracy concerns correctness of values and completeness concerns missing data.
- ✗
Completeness
Why it's wrong here
Completeness concerns missing values or records, whereas every row here contains a date. Format inconsistency is a consistency problem, not an absence of data; completeness would apply if some records had no date field populated at all.
- ✗
Timeliness
Why it's wrong here
Timeliness concerns whether data is available when needed, not how values are formatted. Inconsistent date representations leave the data present and current, so timeliness is untouched; it would be the answer if records arrived too late for the analysis.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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