MLS-C01 Modeling Practice Question
A company is building a recommendation system for an e-commerce platform. The system needs to suggest products to users based on past purchases and browsing history. Which approach would be most appropriate for this use case?
⚠ Common exam trap
AWS often tests the distinction between collaborative filtering and matrix factorization, where candidates mistakenly choose matrix factorization (Option D) because it is a popular technique, but the question's emphasis on 'past purchases and browsing history' (implicit feedback) makes collaborative filtering the more direct and practical choice, as matrix factorization typically requires explicit ratings or careful adaptation for implicit data.
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 using past user-item interactions
Collaborative filtering is the most appropriate approach because it leverages past user-item interactions (e.g., purchases, clicks) to identify patterns and recommend items that similar users have liked. This method directly captures user behavior and preferences without requiring explicit product metadata, making it ideal for e-commerce recommendation systems where implicit feedback is abundant.
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 using product descriptions
Why it's wrong here
Content-based filtering does not leverage user behavior data, which is key for this use case.
- ✗
K-means clustering of users based on demographics
Why it's wrong here
Clustering alone does not generate personalized recommendations.
- ✓
Collaborative filtering using past user-item interactions
Why this is correct
Collaborative filtering leverages user behavior patterns to make recommendations.
- ✗
Matrix factorization on user-item ratings
Why it's wrong here
Matrix factorization is a specific technique under collaborative filtering, but collaborative filtering is the broader correct category.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLS-C01 exam.