AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 160

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?

Answer options

• A. Use the CatBoost algorithm to recommend the next airport destination.
• B. Use the DeepAR forecasting algorithm to recommend the next airport destination.
• C. Use the Factorization Machines algorithm to recommend the next airport destination.
• D. Use the k-means algorithm to cluster users into groups. Map each group to the next airport destination based on user search history.

Correct answer: C

Explanation

The Factorization Machines algorithm is well-suited for handling high-dimensional sparse data, making it ideal for this scenario with one-hot encoded categorical features. CatBoost and DeepAR are not designed specifically for this type of recommendation problem, while the k-means algorithm is primarily for clustering rather than direct recommendations.