AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 150

An ML engineer wants to use Amazon SageMaker AI to prepare data for training. During exploratory data analysis, the ML engineer notices that several categorical features are missing values.

How can the ML engineer use SageMaker AI to solve this problem?

Answer options

• A. Use SageMaker Clarify to impute categorical features with the mean value.
• B. Use SageMaker Clarity to impute categorical features with the mode value.
• C. Use SageMaker Data Wrangler to impute categorical features with the mean value.
• D. Use SageMaker Data Wrangler to impute categorical features with the mode value.

Correct answer: D

Explanation

The correct answer is D because SageMaker Data Wrangler is specifically designed for data preparation tasks, and imputing missing values in categorical features is typically done using the mode, or most frequent value, rather than the mean. Options A and C incorrectly suggest using the mean, which is not appropriate for categorical data, while option B refers to SageMaker Clarity, which is not the correct tool for this task.