nerdexam
Microsoft

DP-100 · Question #478

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might h

The correct answer is A. Yes. Setting regenerate_outputs=True on Azure ML pipeline steps forces all steps to rerun, bypassing cached outputs so updated data is fully processed.

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Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step. You must update the content of the downstream data source of pipeline1 and run the pipeline again. You need to ensure the new run of pipeline1 fully processes the updated content. Solution: Set the allow_reuse parameter of the PythonScriptStep object of both steps to False. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(26 responses)
  • A
    92% (24)
  • B
    8% (2)

Why each option

Setting regenerate_outputs=True on Azure ML pipeline steps forces all steps to rerun, bypassing cached outputs so updated data is fully processed.

AYesCorrect

Setting the regenerate_outputs parameter to True disables Azure ML's step reuse and caching mechanism, forcing every pipeline step to execute fresh regardless of whether inputs appear unchanged by hash. This ensures updated downstream data source content is fully ingested and processed through the entire pipeline. Without this setting, Azure ML may skip steps whose inputs appear identical, missing the updated content.

BNo

Answering No is incorrect because setting regenerate_outputs=True is a valid and supported approach to force full pipeline reprocessing when data source content changes.

Concept tested: Azure ML pipeline step caching and regenerate_outputs parameter

Source: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-machine-learning-pipelines

Topics

#Azure Machine Learning Pipelines#Pipeline Caching#PythonScriptStep#allow_reuse parameter

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