AI0-001 AI Concepts and Techniques Practice Question
A company wants to use machine learning to recommend products to customers based on their purchase history. Which TWO techniques are appropriate for this task? (Select TWO)
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
✓
Collaborative filtering
Collaborative filtering (A) is correct because it is the canonical recommendation technique that leverages patterns across users' purchase histories (user-item interaction matrices) to predict products a customer is likely to buy, either via user-based or item-based similarity. K-Nearest Neighbors (C) is also correct because it can be applied to recommendation by finding the k most similar users or items based on historical purchase vectors and aggregating their preferences to generate recommendations. PCA (B) is a dimensionality-reduction technique, not a recommender, though it may be used as preprocessing. Naive Bayes (D) is a probabilistic classifier for labeled categories such as spam detection, not suited to ranking product recommendations from purchase history. Linear regression (E) predicts a continuous numeric value and does not model user-item preference relationships needed for product recommendation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Collaborative filtering
Why this is correct
Collaborative filtering recommends items by exploiting similarity patterns across users' purchase histories — users who bought similar items receive comparable suggestions. It requires no item content metadata, matching the stem's purchase-history-only input, and scales well for product recommendation.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA performs dimensionality reduction, not recommendation; it finds principal components in feature data. It is tempting because it is genuinely useful preprocessing before modelling, and would be correct where high-dimensional purchase data needs compressing to reduce overfitting before a recommender is trained.
- ✓
K-Nearest Neighbors (KNN)
Why this is correct
K-Nearest Neighbors is a supervised technique that predicts a target from labelled examples. Applied to purchase history, it finds customers with similar buying patterns and recommends items those neighbours bought, making it appropriate for product recommendation.
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Naive Bayes
Why it's wrong here
Naive Bayes is a supervised classifier predicting a category label, not ranking items by preference for a user. It is tempting because it handles sparse purchase-history features well, and would be correct for tasks such as classifying whether a customer will buy a specific product.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts a continuous numeric value from features, producing no ranked item list for a user. It is tempting because it is simple and interpretable on purchase-history data, and would be correct for forecasting spend or quantity purchased rather than recommending products.
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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.