Designing and Implementing a Data Science Solution on Azure — Question 39

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 automatically whenever the data in the folder changes.
What should you do?

Answer options

• 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

Correct answer: D

Explanation

The correct answer is D because creating a Schedule for the pipeline with the proper datastore and path settings allows it to trigger based on changes in the folder. Option A does not ensure automatic triggering based on data changes. Option B, while it discusses scheduling, incorrectly uses 'auto' as a frequency, which is not a valid option. Option C does not facilitate automatic triggering but merely sets a parameter for the pipeline.