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 is unsupervised and groups data based on feature similarity.
- ✗
Random forest classifier
Why it's wrong here
Random forest is a supervised classification algorithm.
- ✗
Linear regression
Why it's wrong here
Linear regression is supervised and predicts continuous values.
- ✗
Support vector machine
Why it's wrong here
SVM is supervised and used for classification or regression.
About these practice questions
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JA
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.