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Answer: Upload a .yaml file to Workspace1.
The question asks for the first action to make a custom component available in an Azure ML designer pipeline. Option E (Upload a .yaml file to Workspace1) is correct because custom components in Azure ML are defined using YAML files that specify the component's interface, code, and environment. Uploading the YAML file registers the component in the workspace, making it available for use in designer pipelines. While the community discussion includes a comment suggesting datastore creation (D) as a prerequisite, this is not the first specific action for custom components—datastores are for data access, not component registration. The consensus and upvoted answers in the discussion support E, as it directly addresses the requirement to include a custom component by registering it via YAML.
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You have an Azure Machine Learning workspace named Workspace1.
You plan to create a pipeline using the Azure Machine Learning Studio designer that includes a custom component.
What is the first action you must take to ensure the custom component is available for use in the pipeline?
A
Create a pipeline endpoint.
B
Add a linked service to Workspace1.
C
Upload a .json file to Workspace1.
D
Create a datastore.
E
Upload a .yaml file to Workspace1.
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