DA0-002 Data Analysis Practice Question
A company wants to segment its customers into distinct groups based on purchasing behavior. Which algorithm is best suited for this task?
⚠ Common exam trap
Test-takers frequently confuse supervised learning algorithms (like decision trees or logistic regression) with unsupervised clustering, mistakenly thinking that any algorithm that 'groups' data can be used for segmentation without recognizing the need for unlabeled data.
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
✓
K-means clustering
K-means clustering is an unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity, making it ideal for segmenting customers by purchasing behavior without predefined labels. It groups customers who exhibit similar purchasing patterns, enabling the company to identify natural segments for targeted marketing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decision tree
Why it's wrong here
Decision trees perform supervised classification or regression against a known label, so they cannot discover groupings without predefined segment labels. They suit predicting a known outcome, such as churn, from customer attributes; unsupervised partitioning of unlabelled purchase data requires clustering instead.
- ✗
Logistic regression
Why it's wrong here
Logistic regression predicts a binary or categorical outcome from labelled training data, so it cannot partition customers into unlabelled groups. It would be the right choice for classifying customers as likely to churn versus not, given historical labels; segmentation without labels needs clustering.
- ✓
K-means clustering
Why this is correct
K-means clustering partitions unlabelled records into k groups by minimising within-cluster variance, using distance between feature vectors. It suits segmentation on purchasing behaviour, where no predefined labels exist, satisfying the requirement to form distinct customer groups.
- ✗
Linear regression
Why it's wrong here
Linear regression models a continuous numeric target from labelled data, producing a predicted value rather than group membership. It fits forecasting, such as predicting next quarter's spend from history; partitioning unlabelled customers into behavioural segments requires an unsupervised clustering algorithm.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.