AI0-001 Implementing AI Solutions Practice Question
A machine learning engineer is building a recommendation system for an e-commerce platform. The system should suggest products based on user purchase history and browsing behavior. Which model selection 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
✓
Collaborative filtering model (e.g., matrix factorization)
Collaborative filtering models (e.g., matrix factorization) are effective for recommendation tasks using user-item interaction data. Linear regression is for regression, not recommendation. Image classification is unrelated. Random forests can be used but are less common for collaborative filtering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image classification model (e.g., CNN)
Why it's wrong here
Image classification is for labeling images, not for recommending products based on behavior.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts a continuous value; it is not suitable for recommending items based on user-item interactions.
- ✗
Random forest classifier
Why it's wrong here
Random forest can be used but typically requires feature engineering; collaborative filtering is more standard and effective for this use case.
- ✓
Collaborative filtering model (e.g., matrix factorization)
Why this is correct
Collaborative filtering leverages patterns of user-item interactions to make personalized recommendations, ideal for this scenario.
About these practice questions
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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.