DP-100 · Question #238
You use the Azure Machine Learning Python SDK to define a pipeline to train a model. The data used to train the model is read from a folder in a datastore. You need to ensure the pipeline runs automat
The correct answer is D. Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder. To trigger a pipeline automatically when data changes in a specific datastore folder, you create a Schedule object and set its datastore property to the monitored datastore and the path_on_datastore property to the specific folder. Azure ML polls that path and triggers the pipeli
Question
Options
- ASet the regenerate_outputs property of the pipeline to True
- BCreate a ScheduleRecurrance object with a Frequency of auto. Use the object to create a
- CCreate a PipelineParameter with a default value that references the location where the training
- DCreate a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder
How the community answered
(24 responses)- A8% (2)
- B13% (3)
- C4% (1)
- D75% (18)
Explanation
To trigger a pipeline automatically when data changes in a specific datastore folder, you create a Schedule object and set its datastore property to the monitored datastore and the path_on_datastore property to the specific folder. Azure ML polls that path and triggers the pipeline whenever new or modified files are detected. Option A (regenerate_outputs=True) forces step re-execution regardless of data changes but does not create an automatic trigger. Option B is invalid because Frequency has no 'auto' value. Option C (PipelineParameter) parameterizes pipeline inputs but does not create an event-driven trigger.
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