AI0-001 AI Concepts and Techniques Practice Question
A company wants to recommend products to users based on their past purchase history. Which machine learning paradigm is BEST suited for this 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
✓
Supervised learning with regression
Recommender systems are a classic application of supervised learning (if using regression or classification to predict ratings) or unsupervised learning (collaborative filtering). Among the options, supervised learning with regression is appropriate for predicting purchase likelihood.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning is for sequential decision-making in dynamic environments, not static recommendation.
- ✗
Unsupervised clustering
Why it's wrong here
Clustering can segment users but does not directly predict which product a user will buy.
- ✓
Supervised learning with regression
Why this is correct
Supervised regression can predict the likelihood or rating of a product for a user based on historical data.
- ✗
Self-supervised learning
Why it's wrong here
Self-supervised learning is typically used for pre-training representations, not directly for recommendation.
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.