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Answer: 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
Option D is correct because it creates a file-reactive schedule that monitors a specific folder in a datastore for changes, which automatically triggers the pipeline when data is modified. This aligns with Azure Machine Learning's change-based scheduling feature, where specifying the datastore and path_on_datastore enables monitoring. The community discussion, with 100% consensus and upvoted comments referencing official documentation, confirms this approach. Option A is incorrect as regenerate_outputs relates to rerunning pipeline steps, not triggering on data changes. Option B is unsuitable because a ScheduleRecurrence with 'auto' frequency does not exist; schedules based on time intervals won't react to data changes. Option C is incorrect as PipelineParameter allows parameterization but doesn't enable automatic triggering based on data modifications.
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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
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