Answer-first summary for fast verification
Answer: Yes
The solution meets the goal because setting `allow_reuse=False` for both PythonScriptStep objects ensures that neither step reuses outputs from previous runs, forcing both steps to execute fully with the updated data. This is the correct approach when the downstream data source has changed, as it prevents Azure ML's default caching behavior from skipping steps based on unchanged inputs. The community discussion strongly supports this with 100% selecting 'Yes' and upvoted comments explaining that `allow_reuse=False` guarantees re-execution. Alternative suggestions about `regenerate_outputs` are less suitable here because that parameter applies at the pipeline submission level, not individual steps, and the question specifically addresses step-level control via `allow_reuse`.
Author: LeetQuiz Editorial Team
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You create an Azure Machine Learning pipeline named pipeline1 that has two PythonScriptStep objects. Data from the first step is passed to the second step.
You update the data source for the downstream step and must run the pipeline again to fully process the updated data.
You set the allow_reuse parameter for both PythonScriptStep objects to False.
Does this solution ensure the new run of pipeline1 fully processes the updated data?
A
Yes
B
No
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