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You are using the Azure Machine Learning Python SDK to define a training pipeline. The training data is loaded from a specific folder in a datastore.
What should you do to configure the pipeline to trigger automatically whenever the data in that folder changes?
A
Set the regenerate_outputs property of the pipeline to True
B
Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline
C
Create a PipelineParameter with a default value that references the location where the training data is stored
D
Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datastore property
