MLA-C01 · Question #150
MLA-C01 Question #150: Real Exam Question with Answer & Explanation
The correct answer is B: Factorization Machines. The Factorization Machines algorithm in SageMaker is specifically designed for recommendation systems and works well with high-dimensional sparse data such as user-item interactions. It efficiently models variable interactions and is the best choice for building a recommendation
Question
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?
Options
- AK-nearest neighbors (k-NN)
- BFactorization Machines
- CPrincipal component analysis (PCA)
- DSequence-to-Sequence (seq2seq)
Explanation
The Factorization Machines algorithm in SageMaker is specifically designed for recommendation systems and works well with high-dimensional sparse data such as user-item interactions. It efficiently models variable interactions and is the best choice for building a recommendation model in this scenario.
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