Clip Outliers is the correct choice.

The Clip Outliers component is specifically designed to detect and remove or cap extreme values in a single feature column so that those values do not unduly influence model training. This component provides options to set thresholds or use statistical rules to clip values that fall outside an expected range.

Impute Missing Values is incorrect because it is used to fill in missing or null entries in a dataset and it does not remove or clip extreme values.

Scale Features is incorrect because it rescales numeric features to a common range or variance and it does not explicitly remove outliers by clipping them.

Normalize Data is incorrect because it adjusts vectors to a common norm or range and it is not a mechanism for removing or capping extreme values.

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