You manage an Azure Machine Learning workspace. You choose the uri_folder data type as an output of a pipeline component. You need to define the data access mode that is supported by your…

The correct answer is B. rw_mount. The rw_mount (read-write mount) access mode is the correct choice for a uri_folder output in a pipeline component. Outputs require write access, so only modes that support writing are valid. rw_mount mounts the storage location with both read and write permissions, allowing the…

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Question

You manage an Azure Machine Learning workspace. You choose the uri_folder data type as an output of a pipeline component. You need to define the data access mode that is supported by your configuration. Which mode should you define?

Options

Aeval_upload
Brw_mount
Cdownload
Dro_mount

How the community answered

(57 responses)

A
5% (3)
B
72% (41)
C
7% (4)
D
16% (9)

Explanation

The rw_mount (read-write mount) access mode is the correct choice for a uri_folder output in a pipeline component. Outputs require write access, so only modes that support writing are valid. rw_mount mounts the storage location with both read and write permissions, allowing the component to write output files. ro_mount (read-only mount) and download are input modes and do not permit writing. eval_upload is not a standard Azure ML data access mode for pipeline components.

Topics

#Azure ML Pipelines#Data access modes#uri_folder#Pipeline outputs

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