MLA-C01 Question #212: Real Exam Question with Answer & Explanation

Question

A travel company wants to create an ML model to recommend the next airport destination for its users. The company has collected millions of data records about user location, recent search history on the company's website, and 2,000 available airports. The data has several categorical features with a target column that is expected to have a high-dimensional sparse matrix. The company needs to use Amazon SageMaker AI built-in algorithms for the model. An ML engineer converts the categorical features by using one-hot encoding. Which algorithm should the ML engineer implement to meet these requirements?

Options

• AUse the CatBoost algorithm to recommend the next airport destination.
• BUse the DeepAR forecasting algorithm to recommend the next airport destination.
• CUse the Factorization Machines algorithm to recommend the next airport destination.
• DUse the k-means algorithm to cluster users into groups. Map each group to the next airport

Explanation

Factorization Machines are designed to work efficiently with high-dimensional sparse feature spaces that result from one-hot encoding of categorical variables. This makes them well suited for large-scale recommendation problems, such as predicting the next airport destination from user behavior and categorical inputs, and they are available as a SageMaker built-in algorithm.

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