This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 machine learning team is building a recommendation system for an e-commerce platform. They have user-item interaction data (clicks, purchases). They need to choose an algorithm that can capture both user and item latent factors and handle missing data. Which algorithm should they use?
Linear regression
Why wrong: Linear regression does not capture latent factors.
Principal component analysis (PCA)
Why wrong: PCA is for dimensionality reduction, not recommendation.
Matrix factorization
Matrix factorization learns latent factors and handles missing data.
Convolutional neural network (CNN)
Why wrong: CNNs are for spatial data like images.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Matrix factorization
Matrix factorization is the correct choice because it decomposes the user-item interaction matrix into lower-dimensional latent factors for users and items, capturing underlying patterns in preferences. It naturally handles missing data by learning from observed interactions only, making it ideal for recommendation systems with sparse data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Linear regression
Why it's wrong here
Linear regression does not capture latent factors.
Principal component analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction, not recommendation.
Matrix factorization
Why this is correct
Matrix factorization learns latent factors and handles missing data.
Related concept
Read the scenario before looking for a memorised answer.
Convolutional neural network (CNN)
Why it's wrong here
CNNs are for spatial data like images.
Common exam traps
The trap here is that candidates may choose PCA because it also performs dimensionality reduction, but PCA cannot handle missing data or model user-item interactions for collaborative filtering, which is the core requirement of the question.
Detailed technical explanation
Matrix factorization, such as in Funk SVD or alternating least squares (ALS), learns user and item embeddings by minimizing reconstruction error on observed ratings, often using regularization to prevent overfitting. In real-world e-commerce, it scales to millions of users and items via stochastic gradient descent, and implicit feedback variants (e.g., weighted ALS) handle clicks and purchases without explicit ratings.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Matrix factorization — Matrix factorization is the correct choice because it decomposes the user-item interaction matrix into lower-dimensional latent factors for users and items, capturing underlying patterns in preferences. It naturally handles missing data by learning from observed interactions only, making it ideal for recommendation systems with sparse data.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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