The correct option is Register the updated files using the same dataset name as the previously registered dataset.

Registering updated files under the same dataset name causes Azure Machine Learning to create a new version of the dataset while preserving prior versions. The registry records the data paths and metadata and increments the dataset version so you can reference the specific version used in experiments and reproduce results.

Datasets will version automatically on a schedule that you configure is incorrect because Azure Machine Learning does not provide built in scheduled versioning. You can automate registration with scripts or pipelines to mimic a schedule but the service only creates a new version at registration time.

Start a new training experiment that references the prior dataset and save the output as a separate dataset is incorrect because running training that uses an existing dataset does not produce a new version of that dataset. The outputs of a run are separate artifacts and do not increment the original dataset version unless you explicitly register the updated files with the same dataset name.

Load the updated data during a run and then register it as a dataset is misleading and therefore incorrect in this context because simply loading data in a run does not by itself version the prior dataset. You must explicitly register the updated files using the same dataset name if you want a new version to be recorded in the dataset registry.

Full AWS Practitioner Certification Question