DA0-002 Data Acquisition and Preparation Practice Question
A retail company wants to analyze customer purchase patterns to identify products frequently bought together. Which data mining technique is most appropriate?
⚠ Common exam trap
It's easy for candidates to confuse association rules with clustering because both are unsupervised and used for pattern discovery, but clustering groups similar items while association rules find co-occurrence relationships between items in transactions.
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
✓
Association rules
Association rules are specifically designed to uncover relationships between items in transactional datasets, such as 'customers who buy X also buy Y.' This technique generates rules like {bread, butter} → {milk} with metrics such as support, confidence, and lift, directly answering the question of which products are frequently bought together. Classification, clustering, and regression serve different purposes: they predict labels, group similar instances, or model continuous relationships, respectively. Therefore, association rules are the most appropriate choice for market basket analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Classification
Why it's wrong here
Classification predicts a predefined label for each record, so it cannot surface co-occurrence relationships between products. It is tempting because classification is a core data mining technique, yet it would be correct only when the goal is assigning known categories, such as flagging customers as churners.
- ✗
Clustering
Why it's wrong here
Clustering groups similar records by overall feature similarity, so it does not produce the item-pair co-occurrence rules that basket analysis requires. It is tempting because clustering is a core data mining technique, yet it would be correct only for segmenting customers into like groups, not for finding products bought together.
- ✗
Regression
Why it's wrong here
Regression models a continuous numeric outcome from predictor variables, so it cannot reveal which products are purchased together. It is tempting because regression is a standard data mining technique, yet it would be correct only when forecasting a quantity, such as predicting next month's sales revenue.
- ✓
Association rules
Why this is correct
Association rules discover co-occurrence relationships between items in transactional data, directly satisfying the requirement to identify products frequently bought together. Algorithms such as Apriori and FP-Growth generate rules like {bread} → {butter}, quantified by support, confidence and lift, which is precisely the market-basket analysis this retail scenario demands.
Go deeper
Related to this question
About these practice questions
Courseiva writes every DA0-002 question from scratch — 1,004 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.