Answer-first summary for fast verification
Answer: Yes
The solution meets the goal because setting the `regenerate_outputs` parameter to `True` forces regeneration of all step outputs and disallows data reuse from previous runs. This ensures that when the downstream data source is updated, the pipeline will fully reprocess all data rather than potentially reusing cached outputs from previous executions. The community discussion confirms this with upvoted comments referencing Microsoft documentation that states `regenerate_outputs` when set to True will force regeneration of all step outputs and disallow data reuse, which is exactly what's needed when source data has been updated.
Author: LeetQuiz Editorial Team
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You create an Azure Machine Learning pipeline named pipeline1 that has two steps containing Python scripts. The first step passes processed data to the second step.
You update the content of the downstream data source for pipeline1 and must run the pipeline again. You need to ensure the new run of pipeline1 fully processes the updated content.
Solution: You set the regenerate_outputs parameter of the run submit method for the pipeline1 experiment to True.
Does the solution meet the goal?
A
Yes
B
No
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