MLS-C01 Modeling Practice Question
A startup is building a recommendation system for an e-commerce platform using collaborative filtering. They have a dataset of user-item interactions (ratings) with 1 million users and 100,000 items. The data is sparse (99% missing ratings). They need to train a model on Amazon SageMaker that can handle large-scale sparse data efficiently. Which approach should they use?
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
✓
Use the built-in Factorization Machines algorithm in SageMaker
SageMaker's Factorization Machines handle sparse data efficiently and are designed for recommendation tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use PCA to reduce dimensionality and then apply k-nearest neighbors
Why it's wrong here
PCA is not suited for missing values and collaborative filtering.
- ✓
Use the built-in Factorization Machines algorithm in SageMaker
Why this is correct
Factorization Machines are designed for sparse data and scale well.
- ✗
Use the built-in XGBoost algorithm with one-hot encoding for user and item IDs
Why it's wrong here
One-hot encoding would create a huge feature space and is inefficient.
- ✗
Implement a neural network with dense layers using the built-in MXNet framework
Why it's wrong here
Dense layers do not handle sparsity well and may overfit.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is building a recommendation system for an e-commerce platform. The data includes user IDs and item IDs. Which SageMaker built-in algorithm is most appropriate?
easy- A.BlazingText
- B.XGBoost
- ✓ C.Factorization Machines
- D.Image Classification
Why C: Factorization Machines (FM) are specifically designed for recommendation tasks with sparse, high-dimensional categorical data like user IDs and item IDs. They model pairwise interactions between features (e.g., user-item interactions) using factorized parameters, making them highly effective for collaborative filtering and implicit feedback scenarios in e-commerce.
Variation 2. A company is building a recommendation system for an e-commerce platform. The data includes user-item interactions and features such as user demographics and item categories. Which algorithm would be most appropriate for generating personalized recommendations?
medium- A.XGBoost
- ✓ B.Factorization Machines
- C.k-means clustering
- D.Principal Component Analysis (PCA)
Why B: Factorization Machines (FM) are specifically designed for recommendation tasks with sparse, high-dimensional data like user-item interactions. They model pairwise feature interactions (e.g., user demographics × item categories) using factorized parameters, enabling personalized recommendations even when many user-item pairs are unobserved. This makes FM far more effective than tree-based or clustering methods for collaborative filtering and feature-rich recommendation scenarios.
JA
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.