You use the Azure Machine Learning SDK for Python to create a pipeline that includes the following step: The output of the step run must be cached and reused on subsequent runs when the…

The correct answer is A. allow_reuse. The allow_reuse parameter in an Azure Machine Learning pipeline step definition controls whether the step's output is cached and reused in subsequent runs when inputs and settings (including source_directory) have not changed. Setting allow_reuse=True tells the pipeline service…

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Question

You use the Azure Machine Learning SDK for Python to create a pipeline that includes the following step: The output of the step run must be cached and reused on subsequent runs when the source_directory value has not changed. You need to define the step. What should you include in the step definition?

Options

Aallow_reuse
Bversion
Cdata.as_input(name=?
Dhash_paths

How the community answered

(21 responses)

A
95% (20)
B
5% (1)

Explanation

The allow_reuse parameter in an Azure Machine Learning pipeline step definition controls whether the step's output is cached and reused in subsequent runs when inputs and settings (including source_directory) have not changed. Setting allow_reuse=True tells the pipeline service to compare the step's inputs, parameters, and source directory against previous runs and reuse the cached output if nothing has changed, saving time and compute costs. The 'version' parameter is a string identifier for tracking purposes but does not control caching behavior. 'data.as_input()' specifies how a dataset is consumed as input, and 'hash_paths' is a legacy parameter for specifying additional paths to hash when determining reuse eligibility.

Topics

#Azure Machine Learning Pipelines#Step Caching#Python SDK#Pipeline Optimization

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