MLA-C01 · Question #212
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 t
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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
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