AI0-001 Machine Learning and Deep Learning Practice Question
A machine learning engineer needs to choose an algorithm for grouping customers into segments based on purchasing behavior without any labels. Which algorithm should the engineer use?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between supervised and unsupervised learning, and the trap here is that candidates may confuse clustering with classification, picking a supervised algorithm like Random Forest or SVM because they think of 'grouping' as a classification task.
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 groups unlabeled data into clusters based on feature similarity, making it ideal for segmenting customers by purchasing behavior without predefined labels. It partitions data into K clusters by minimizing within-cluster variance, which directly addresses the requirement of discovering natural groupings in the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
K-means clustering
Why this is correct
K-means clustering partitions unlabelled data into k groups by minimising within-cluster variance, directly satisfying the stem's requirement to segment customers without labels. Supervised alternatives need target labels, which are absent here, so K-means fits the unsupervised grouping constraint.
- ✗
Random forest classifier
Why it's wrong here
Random forest classifier is supervised, training on labelled class examples to predict known categories, so it cannot form segments from unlabelled data. It fits classification where historical labels exist, not the unsupervised clustering required here.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts a continuous numeric target from labelled data, so it cannot assign unlabelled customers to discrete groups. It is tempting as a familiar baseline model, and would be correct when forecasting a value such as expected spend from labelled examples.
- ✗
Support vector machine
Why it's wrong here
A support vector machine is supervised, requiring labelled examples to learn a decision boundary, so it cannot discover segments in unlabelled purchasing data. It suits classification or regression with known labels, not the unsupervised grouping this scenario demands.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.