MLA-C01 · Question #181
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 correct answer is D. Use SageMaker Data Wrangler to impute categorical features with the mode value. SageMaker Data Wrangler provides built-in data preparation capabilities, including handling missing values. For categorical features, imputing missing values with the mode preserves valid category values and is an appropriate, supported approach during data preparation.
Question
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?
Options
- AUse SageMaker Clarify to impute categorical features with the mean value.
- BUse SageMaker Clarity to impute categorical features with the mode value.
- CUse SageMaker Data Wrangler to impute categorical features with the mean value.
- DUse SageMaker Data Wrangler to impute categorical features with the mode value.
How the community answered
(48 responses)- A2% (1)
- B10% (5)
- C8% (4)
- D79% (38)
Explanation
SageMaker Data Wrangler provides built-in data preparation capabilities, including handling missing values. For categorical features, imputing missing values with the mode preserves valid category values and is an appropriate, supported approach during data preparation.
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