Turn on a native switch to train solely on a fraction of the input dataset is the correct setting that is not available to change during model creation.

Automated Machine Learning workflows require any dataset subsetting or sampling to be handled before the AutoML run starts or through a separate preprocessing step. There is no built in toggle during the model creation UI that simply tells the system to train only on a fraction of the input data and nothing else.

Set the experiment timeout to 90 minutes is incorrect because AutoML configurations let you set a maximum run duration or timeout when you create the experiment.

Select R squared as the main evaluation metric is incorrect because you can choose the evaluation metric for the task type and R squared is available for regression tasks in automated experiment settings.

Limit training to only the XGBoost algorithm is incorrect because AutoML platforms commonly allow you to include or exclude specific algorithms or to provide a custom list so you can restrict the search to XGBoost if you want.

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