AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 130

A company is planning to use an Amazon SageMaker prebuilt algorithm to create a recommendation model. The algorithm must be able to make predictions on high-dimensional sparse data.

Which SageMaker algorithm should the company choose for the recommendation model?

Answer options

• A. K-nearest neighbors (k-NN)
• B. Factorization Machines
• C. Principal component analysis (PCA)
• D. Sequence-to-Sequence (seq2seq)

Correct answer: B

Explanation

The correct choice is B, Factorization Machines, as they are specifically designed to handle high-dimensional sparse data and are effective for recommendation tasks. Option A, K-nearest neighbors, is not suitable for sparse data due to its reliance on distance calculations. Option C, Principal component analysis, is a dimensionality reduction technique, not a predictive algorithm. Option D, Sequence-to-Sequence, is used for tasks such as translation and not for recommendation systems.