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DP-100 · Question #317

You manage an Azure Machine Learning workspace named Workspace1. You plan to create a pipeline in the Azure Machine Learning Studio designer. The pipeline must include a custom component. You need…

The correct answer is E. Upload a .yaml file to Workspace1. To use a custom component in Azure ML Studio designer, you must first upload a YAML specification file that defines the component's interface, environment, and implementation.

Design and prepare a machine learning solution

Question

You manage an Azure Machine Learning workspace named Workspace1. You plan to create a pipeline in the Azure Machine Learning Studio designer. The pipeline must include a custom component. You need to ensure the custom component can be used in the pipeline. What should you do first?

Options

  • ACreate a pipeline endpoint.
  • BAdd a linked service to Workspace1.
  • CUpload a .json file to Workspace1.
  • DCreate a datastore.
  • EUpload a .yaml file to Workspace1.

How the community answered

(40 responses)
  • B
    3% (1)
  • C
    8% (3)
  • D
    3% (1)
  • E
    88% (35)

Why each option

To use a custom component in Azure ML Studio designer, you must first upload a YAML specification file that defines the component's interface, environment, and implementation.

ACreate a pipeline endpoint.

A pipeline endpoint is used to publish and version a completed pipeline for batch or real-time inference, not to define or register custom components.

BAdd a linked service to Workspace1.

A linked service connects the Azure ML workspace to external compute services such as Azure Synapse Analytics, and has no role in registering custom pipeline components.

CUpload a .json file to Workspace1.

Azure ML custom component specifications must be authored in YAML format, not JSON, so uploading a .json file would not register a valid component.

DCreate a datastore.

A datastore defines a connection to an external storage service for data access and is unrelated to defining or registering component logic.

EUpload a .yaml file to Workspace1.Correct

Custom components in Azure ML designer are defined using YAML specification files that declare inputs, outputs, the execution environment, and the command to run. Uploading this YAML file to the workspace registers the component definition, making it available in the designer's component palette for use in pipelines.

Concept tested: Registering custom components in Azure ML designer

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

Topics

#Azure ML Components#ML Pipelines#Azure ML Designer#YAML Definitions

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