DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is performing data profiling on a customer table. Which metric would best help identify missing values in the 'phone' column?
⚠ Common exam trap
The trap is confusing cardinality with completeness — candidates see 'cardinality' and think it measures how many values are present, but it actually measures distinct values, not missing ones.
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
✓
Null count
The null count metric directly measures the number of missing (NULL) values in a column, which is exactly what the analyst needs to identify missing phone numbers. Data profiling tools report null count per column as a standard completeness metric. Other metrics like cardinality or mean do not reveal missingness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cardinality
Why it's wrong here
Cardinality counts distinct values, revealing duplication or uniqueness, not absent entries. A phone column could show high cardinality while still containing many nulls. Null profiling, which tallies missing or empty records per column, is the metric that directly surfaces absent values.
- ✓
Null count
Why this is correct
Null count directly quantifies absent entries in the phone column, satisfying the profiling goal of identifying missing values. Unlike distinct count or data type checks, it measures completeness per attribute, exposing the exact volume of nulls requiring remediation before analysis.
- ✗
Mean
Why it's wrong here
Mean averages numeric values, so it cannot be computed on a text phone column and says nothing about absent entries. It suits detecting skew or central tendency in quantitative fields. Null percentage or completeness profiling is what quantifies missing values.
- ✗
Row count
Why it's wrong here
Row count gives the table's total records, not how many lack a phone number. Comparing it against non-null counts yields completeness, but the raw figure alone cannot identify missing values. A null-count or completeness metric per column is required.
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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
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