AI0-001 AI Concepts and Techniques Practice Question
A retail analytics team wants to group customers into segments based on purchase frequency, average order value, and recency, without having any predefined segment labels. They plan to use an algorithm that partitions customers into a fixed number of groups by minimizing within-cluster variance. Which technique should they use?
⚠ Common exam trap
Test-takers frequently confuse dimensionality reduction with clustering, since principal component analysis is often mentioned alongside K-means in preprocessing pipelines but does not itself create segments.
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 the correct technique because it is an unsupervised algorithm that partitions observations into a predefined number of clusters by minimizing the sum of squared distances to cluster centroids. The team's goal of grouping customers without labels aligns perfectly with K-means. The other options are either supervised prediction methods or dimensionality reduction techniques that do not produce discrete 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.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts a continuous target from input features and requires labeled outcomes. The team has no predefined segment labels and wants to discover groupings, so regression is inappropriate. It would not partition customers into clusters and would instead attempt to fit a line or hyperplane to predict a numeric value, which does not match the segmentation goal.
- ✓
K-means clustering
Why this is correct
K-means clustering partitions data into a fixed number of groups by minimizing within-cluster variance, exactly matching the described goal. It is unsupervised, so no predefined labels are needed. Given features like purchase frequency, average order value, and recency, K-means will assign customers to the nearest centroid, producing the desired segments.
- ✗
Logistic regression
Why it's wrong here
Logistic regression is a supervised classification algorithm that predicts a categorical label from labeled examples. The retail team has no segment labels, so logistic regression cannot be trained. It would also produce a decision boundary rather than a grouping of customers, making it unsuitable for discovering natural segments in the data.
- ✗
Principal component analysis
Why it's wrong here
Principal component analysis reduces dimensionality by projecting data onto orthogonal components that capture variance. It does not assign customers to discrete groups. While it could be used as a preprocessing step before clustering, the question asks for the technique that partitions customers into a fixed number of groups by minimizing within-cluster variance, which PCA alone does not do.
About these practice questions
This AI0-001 question is part of Courseiva's 962-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 AI0-001 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 AI0-001 exam.