DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is performing data profiling on a customer table. Which metric provides the number of unique values in a column?
⚠ Common exam trap
The trap is confusing cardinality with row count or null count — candidates often assume 'unique values' means total rows, but cardinality specifically counts distinct values, which can be far fewer than the row count.
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
✓
Cardinality
Cardinality refers to the number of distinct (unique) values in a column, which is exactly the metric described. It is a fundamental data profiling statistic used to assess column uniqueness, identify candidate keys, and inform indexing or partitioning decisions. High cardinality columns (e.g., primary keys) have many unique values; low cardinality columns (e.g., status flags) have few.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Row count
Why it's wrong here
Row count returns the total number of records, including duplicates, so it cannot isolate distinct values. It is tempting because it is the most basic profiling metric and is often displayed alongside distinct counts, yet it would be the correct choice when measuring table volume or completeness, not cardinality.
- ✓
Cardinality
Why this is correct
Cardinality counts the distinct values present in a column, directly satisfying the requirement for the number of unique values during data profiling. Unlike row count, which totals all records including duplicates, cardinality reveals value distribution and repetition, exposing low-variance or high-uniqueness columns that affect indexing and query planning decisions.
- ✗
Standard deviation
Why it's wrong here
Standard deviation quantifies the spread of numeric values around the mean, so it says nothing about how many distinct values exist. It is tempting because it is a core profiling statistic for numeric columns, yet it would be the correct choice when assessing variability or outliers, not cardinality.
- ✗
Null count
Why it's wrong here
Null count measures missing entries in a column, not the number of distinct values present. It is tempting because profiling dashboards commonly pair it with distinct counts, and both describe column quality, yet it would be the correct choice when assessing completeness or deciding imputation strategy, not cardinality.
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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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