MLS-C01 Modeling Practice Question
A data scientist is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items, with a sparse user-item interaction matrix. The scientist wants to minimize training time on Amazon SageMaker. Which algorithm would be most appropriate?
⚠ Common exam trap
Many candidates choose XGBoost (D) because of its popularity and strong performance on tabular data, but they overlook that it requires dense feature engineering and is not optimized for the sparse, high-cardinality interaction matrices typical in recommendation systems.
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
✓
Factorization Machines
Factorization Machines (B) are specifically designed for sparse, high-dimensional datasets like the user-item interaction matrix in recommendation systems. They extend matrix factorization by modeling pairwise feature interactions, which is ideal for collaborative filtering tasks. On Amazon SageMaker, the built-in Factorization Machines algorithm is optimized for sparse data and can train efficiently on 1 million users and 100,000 items, minimizing training time compared to general-purpose algorithms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Linear Learner
Why it's wrong here
Linear Learner is for supervised learning, not matrix factorization.
- ✓
Factorization Machines
Why this is correct
Built for recommendation systems with sparse data.
- ✗
K-Means
Why it's wrong here
K-Means is for clustering, not recommendation.
- ✗
XGBoost
Why it's wrong here
XGBoost is for tree-based models, not matrix factorization.
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