The correct option is Set the featurization parameter to "auto".

Set the featurization parameter to "auto" instructs the Automated Machine Learning run to perform automatic featurization. It will impute missing values and apply appropriate encodings to categorical columns, including handling columns with few unique categories, so you do not need to preprocess those fields manually before training.

Set the featurization parameter to "disabled" is incorrect because disabling featurization prevents any automatic preprocessing and the run will not fill missing entries or encode categorical features for you.

Set the featurization parameter to "custom" is incorrect because a custom setting requires you to provide specific featurization rules or a custom pipeline, so automatic imputation and encoding are not guaranteed unless you define them.

Set the featurization parameter to "enabled" is incorrect because it does not represent the automatic heuristic driven featurization provided by Set the featurization parameter to "auto" and therefore does not guarantee the same automatic handling of missing values and categorical transformations.

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