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MLS-C01 Modeling Practice Question

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?

⚠ Common exam trap

Test-takers frequently choose XGBoost (B) because it is a versatile algorithm, but they overlook that FM is purpose-built for sparse, high-dimensional interaction data and directly models pairwise feature interactions without manual feature engineering.

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 (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.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • BlazingText

    Why it's wrong here

    For text classification.

  • XGBoost

    Why it's wrong here

    Not specifically for recommendation.

  • Factorization Machines

    Why this is correct

    Designed for recommendation.

  • Image Classification

    Why it's wrong here

    For images.

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Last reviewed: Jun 24, 2026

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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.