nerdexam
Microsoft

DP-100 · Question #511

You manage an Azure Machine Learning workspace named workspace1. You plan to author custom pipeline components by using Azure Machine Learning Python SDK v2. You must transform the Python code into…

The correct answer is C. mldesigner. The mldesigner library is specifically designed for authoring custom pipeline components in Azure Machine Learning SDK v2. It provides decorators like @command_component that transform Python functions into YAML specifications consumable by the Azure ML pipeline service…

Design and prepare a machine learning solution

Question

You manage an Azure Machine Learning workspace named workspace1. You plan to author custom pipeline components by using Azure Machine Learning Python SDK v2. You must transform the Python code into a YAML specification that can be processed by the pipeline service. You need to import the Python library that provides the transformation functionality. Which Python library should you import?

Options

  • Asklearn
  • Bazure.ai.ml.automl
  • Cmldesigner
  • Dazure.ai.ml.entities

How the community answered

(47 responses)
  • A
    6% (3)
  • B
    2% (1)
  • C
    87% (41)
  • D
    4% (2)

Explanation

The mldesigner library is specifically designed for authoring custom pipeline components in Azure Machine Learning SDK v2. It provides decorators like @command_component that transform Python functions into YAML specifications consumable by the Azure ML pipeline service. sklearn (choice A) is scikit-learn for ML algorithms. azure.ai.ml.automl (choice B) is the AutoML module. azure.ai.ml.entities (choice D) defines ML resource entities (jobs, environments, etc.) but does not provide the component authoring/transformation functionality that mldesigner does.

Topics

#Azure ML pipelines#Custom components#Python SDK v2#mldesigner library

Community Discussion

No community discussion yet for this question.

Sign up to join the discussion
Full DP-100 Practice