Explanation:
The question requires ensuring automated machine learning handles missing value imputation and categorical feature encoding automatically. According to Azure ML documentation and the community consensus (94% selected A with upvoted comments), setting featurization to 'auto' enables AutoML to automatically perform preprocessing steps including imputing missing values and encoding categorical features. Option B ('off') would disable featurization entirely, preventing the required preprocessing. Option C ('on') is not a valid featurization parameter value in Azure AutoML. Option D (FeaturizationConfig) is for custom configurations, which is unnecessary here as the requirements can be met with automatic featurization.
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You are preparing to train a regression model using automated machine learning. The dataset contains features with missing values and categorical features with a small number of distinct values.
You need to configure automated machine learning to meet the following requirements:
Which action should you take?
A
You should make use of the featurization parameter with the 'auto' value pair.
B
You should make use of the featurization parameter with the 'off' value pair.
C
You should make use of the featurization parameter with the 'on' value pair.
D
You should make use of the featurization parameter with the 'FeaturizationConfig' value pair.
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