Question
An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?
- A. Enable workspace-level Git integration and sync assets between repositories.
- B. Publish the asset as a pipeline component.
- C. Create a shared Azure Machine Learning environment that includes the asset.
- D. Publish the asset to an Azure Machine Learning registry. Correct
Explanation
An Azure Machine Learning registry is a central, versioned catalog of models, components, environments and data that any workspace in the organization can reference directly, which is exactly the cross-workspace MLOps scenario of training in dev and deploying in prod. Git sync copies files between repositories rather than sharing one governed asset. A pipeline component or an environment created in a workspace is only visible inside that workspace unless it is itself placed in a registry.
