DA0-002 Data Analysis Practice Question
A retail company wants to segment its customers based on purchase history. Which THREE methods are appropriate for customer segmentation?
⚠ Common exam trap
The trap is that regression and hypothesis-testing methods sound analytical and data-driven, so candidates may select them for segmentation even though they predict values or test differences rather than grouping customers.
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
✓
RFM analysis
RFM analysis (A) is correct because it segments customers by Recency, Frequency, and Monetary value of purchases, directly using purchase history to group customers into meaningful tiers. K-means clustering (C) is correct because it partitions customers into k groups based on feature similarity such as purchase behavior, making it a standard unsupervised segmentation technique. Hierarchical clustering (E) is correct because it builds a dendrogram of nested customer clusters, allowing segmentation at different granularity levels without pre-specifying the number of clusters. Linear regression (B) is not appropriate because it predicts a continuous outcome rather than assigning customers to segments. The t-test (D) is not appropriate because it is a hypothesis test comparing means between two groups, not a segmentation method.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
RFM analysis
Why this is correct
RFM analysis scores customers on recency, frequency and monetary value derived from purchase history, directly satisfying the segmentation requirement. It groups customers into actionable tiers such as best, loyal or at-risk based on transactional behaviour.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts a continuous outcome from input variables; it produces coefficients, not discrete customer groupings. It is tempting because purchase history variables can feed it, and it would be the right choice for forecasting a customer's future spend rather than partitioning customers into segments.
- ✓
K-means clustering
Why this is correct
K-means clustering partitions customers into k groups by minimising within-cluster variance across purchase-history features, directly satisfying the segmentation requirement. Unlike supervised methods, it needs no labelled outcomes, making it appropriate for unlabelled retail transaction data. Choosing k via elbow or silhouette analysis yields actionable customer segments.
- ✗
t-test
Why it's wrong here
A t-test compares means between two groups to test a hypothesis, so it cannot assign customers to segments. It is tempting because analysts do use it to validate whether segments differ on a metric such as average spend, which is a legitimate post-segmentation step rather than a segmentation method itself.
- ✓
Hierarchical clustering
Why this is correct
Hierarchical clustering builds a dendrogram that groups customers by purchase-history similarity without predefining cluster counts, satisfying the retail segmentation requirement. Unlike partitioning methods such as k-means, it reveals nested customer tiers, letting analysts cut the tree at any level. This suits exploratory segmentation where the optimal number of groups is unknown.
Go deeper
Related to this question
About these practice questions
This DA0-002 question is part of Courseiva's 1,004-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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
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.