AI0-001 AI Concepts and Techniques Practice Question
A data scientist is building a recommendation system for an e-commerce platform. The dataset includes user purchase history, product descriptions, and user demographics. The goal is to recommend products that a user is likely to purchase. Which TWO techniques are most appropriate for this task? (Select TWO.)
⚠ Common exam trap
AI0-001 often tests the distinction between recommendation techniques and general ML algorithms, so candidates might incorrectly select linear regression or anomaly detection because they are familiar ML methods, but they do not address the personalization and ranking required for recommendation systems.
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
✓
Content-based filtering
Content-based filtering (A) is correct because it recommends items by matching a user's past purchase history against product descriptions, which is exactly the item-attribute data available here. Collaborative filtering (E) is correct because it leverages patterns across many users' purchase histories to recommend products a similar user is likely to buy, directly addressing the recommendation goal. Together these are the two standard recommender-system techniques suited to user purchase history, product descriptions, and demographics. Association rule mining (B) finds co-occurrence rules like market-basket pairs but does not personalize recommendations to a specific user. Linear regression (C) predicts a continuous numeric value and is not designed for ranking or recommending items. Anomaly detection (D) identifies outliers and is unrelated to generating product recommendations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Content-based filtering
Why this is correct
Content-based filtering matches a user's past purchases against product description features, so it exploits the product descriptions in the dataset and recommends items similar to those the user already bought, without needing other users' data.
- ✗
Association rule mining
Why it's wrong here
Association rule mining finds co-occurrence patterns among items in transactions, such as products frequently bought together, but it does not model individual user preferences from demographics or product descriptions. It is tempting because it powers basket-based recommendations, and would be correct for market-basket analysis rather than personalised prediction for a specific user.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts a continuous numeric outcome from input features, whereas recommending products requires ranking discrete items per user. It is tempting because it is a foundational supervised technique, and would be correct for forecasting a quantity such as expected spend or demand, not for generating a personalised ranked product list.
- ✗
Anomaly detection
Why it's wrong here
Anomaly detection identifies rare observations that deviate from normal patterns, which is the opposite of recommending popular, likely-purchased products. It is tempting because it also uses purchase history, and would be correct for fraud detection or spotting unusual transactions rather than predicting which products a user will buy.
- ✓
Collaborative filtering
Why this is correct
Collaborative filtering satisfies the recommendation goal by exploiting patterns across users' purchase histories, computing similarities between users or items to predict unseen preferences. It directly uses the purchase-history constraint in the stem, unlike content-based methods that rely solely on product descriptions or demographics.
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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.