This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Collaborative filtering can be implemented using memory-based methods (e.g., user-user or item-item nearest neighbors) or model-based methods (e.g., matrix factorization with implicit feedback, such as Alternating Least Squares). In practice, e-commerce platforms often use hybrid approaches that combine collaborative filtering with content-based features to address the cold-start problem for new users or items. The key subtlety is that implicit feedback (e.g., clicks, views) requires special handling, such as confidence weighting or negative sampling, to avoid bias toward popular items.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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