Question

Your team wants to standardize a data preprocessing step so it can be reused across multiple Azure Machine Learning pipelines built by different data scientists. You need to define this step as a versioned, shareable asset in the workspace using the Azure ML CLI (v2). Which asset type should you create and register for the preprocessing step?

Accepted Answer

A command component defined in a YAML spec file, registered with 'az ml component create'

Answer

An environment defined in a YAML spec file, registered with 'az ml environment create'

Answer

A data asset defined as an MLTable, registered with 'az ml data create'

Answer

A managed online endpoint deployed with 'az ml online-endpoint create'