DA0-002 Data Acquisition and Preparation Practice Question
An analyst is sampling a large customer database to estimate the average purchase amount. To ensure that the sample proportionally represents different customer segments (e.g., age groups), which sampling method should be used?
⚠ Common exam trap
DA0-002 often tests sampling method recognition — candidates confuse stratified (proportional representation of known subgroups) with cluster (sampling whole groups) because both involve dividing the population.
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
✓
Stratified sampling
Stratified sampling divides the population into homogeneous subgroups (strata) based on a characteristic like age group, then samples proportionally from each stratum. This guarantees representation of every segment in the sample, which is exactly what the analyst needs to estimate the average purchase amount accurately across customer segments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Systematic sampling
Why it's wrong here
Systematic sampling picks every nth record from the ordered list, which yields proportional segment representation only if the ordering happens to correlate with the segments. It is tempting because it is quick to execute on a database, but stratification requires deliberate partitioning before selection.
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Simple random sampling
Why it's wrong here
Simple random sampling gives every record an equal chance, so small age segments can be under-represented or missed entirely. It is tempting because it is the default unbiased method, but the stem's requirement for proportional representation across segments is exactly what stratified sampling delivers.
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Cluster sampling
Why it's wrong here
Cluster sampling selects whole groups rather than individuals, so it does not guarantee proportional representation of every age segment. It is tempting because it suits geographically dispersed populations where randomising entire clusters cuts survey cost, but the stem demands segment-level proportionality, which stratified sampling provides.
- ✓
Stratified sampling
Why this is correct
Stratified sampling divides the population into distinct strata — here, age groups — then draws proportionally from each, guaranteeing every segment is represented in the sample. This directly satisfies the stem's requirement for proportional representation across customer segments, unlike simple random sampling, which could under-represent smaller groups.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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