Split Data is the correct option for creating separate training and validation datasets inside the Contoso Machine Learning Studio workspace.

The Split Data module is specifically designed to partition a dataset into two or more outputs using a fraction, stratified sampling, or custom filtering. It provides options to control the split ratio and a random seed so that you can reproduce the same training and validation sets across experiments. This makes it the proper choice when you need distinct datasets for model training and validation within Azure Machine Learning Studio.

Group Records into Buckets is incorrect because that operation groups or bins records for aggregation or analysis and it does not produce separate training and validation datasets for model evaluation.

Assign to Clusters is incorrect because that action assigns records to cluster labels after clustering and it is not intended to split a dataset into training and validation parts.

BigQuery is incorrect because it is a Google Cloud data warehouse service and not a module inside Azure Machine Learning Studio, so it is not the tool you would use within the Contoso workspace to split data for model training and validation.

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