DA0-002 Data Acquisition and Preparation Practice Question
A data analyst wants to ensure a sample proportionally represents different regions in a population. Which sampling method should be used?
⚠ Common exam trap
DA0-002 often tests the distinction between stratified sampling (proportional representation of known subgroups) and cluster sampling (sampling whole naturally occurring groups), which candidates frequently confuse 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 distinct subgroups (strata) based on a shared characteristic — here, region — and then draws a proportional random sample from each stratum. This guarantees that each region is represented in the sample in proportion to its size in the population, which is exactly what the analyst requires. Simple random sampling could, by chance, under- or over-represent certain regions.
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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Simple random sampling
Why it's wrong here
Simple random sampling gives every individual an equal chance, but does not guarantee regional proportions match the population, especially with small samples. It is tempting because it is the baseline unbiased method, and it would be correct when the population is homogeneous and no subgroup representation requirement exists.
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Cluster sampling
Why it's wrong here
Cluster sampling selects whole groups, so it represents clusters rather than the population's regional proportions. It is tempting because it is a probability method that reduces cost when regions map to clusters, and it would be correct when the goal is efficient sampling of naturally grouped populations rather than proportional regional representation.
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Systematic sampling
Why it's wrong here
Systematic sampling selects every nth element from a list, so regional proportions depend entirely on how the list happens to be ordered rather than being enforced. It suits evenly ordered frames where periodic selection is unbiased. Stratified sampling is required to guarantee each region's share matches the population.
- ✓
Stratified sampling
Why this is correct
Stratified sampling divides the population into distinct regions (strata) and draws samples from each in proportion to its size, directly satisfying the requirement for proportional regional representation. Unlike simple random sampling, it guarantees every region appears at its correct weight, eliminating the under-representation that random chance can produce.
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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
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.