MLA-C01 · Question #12
MLA-C01 Question #12: Real Exam Question with Answer & Explanation
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Question
Case Study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model. Which action will meet this requirement with the LEAST operational overhead?
Options
- AUse AWS Glue to transform the categorical data into numerical data.
- BUse AWS Glue to transform the numerical data into categorical data.
- CUse Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
- DUse Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.
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