nerdexam
Microsoft

DP-100 · Question #480

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…

The correct answer is B. No. Changing a non-cache-related configuration does not invalidate Azure ML pipeline step outputs and will not force full reprocessing of updated data.

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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: Change the value of the compute_target parameter of the PythonScriptStep object in the two steps. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(48 responses)
  • A
    19% (9)
  • B
    81% (39)

Why each option

Changing a non-cache-related configuration does not invalidate Azure ML pipeline step outputs and will not force full reprocessing of updated data.

AYes

Answering Yes is incorrect because the proposed change does not disable the caching mechanism or affect the step output hash, so the pipeline would still reuse cached results and skip reprocessing the updated data.

BNoCorrect

Azure ML determines whether to reuse a cached step based on input data hashes and source code; changing an unrelated configuration parameter does not invalidate existing cached outputs. Only explicitly setting regenerate_outputs=True or changing a relevant input or script will cause steps to rerun and fully process updated content.

Concept tested: Azure ML pipeline step reuse and cache invalidation

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

Topics

#Azure Machine Learning Pipelines#Pipeline Caching#Data Processing#PythonScriptStep

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