An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production. An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback. You need to change the state of the model version to meet the requirements. What should you do?

Azure Machine Learning's model registry supports three lifecycle states: Active (the default, fully usable state), Archived (not deployable but still accessible for review), and Deleted (permanently removed). Archiving is precisely designed for this scenario: it ensures that operations teams cannot accidentally select the old version for a new deployment, while compliance officers can still inspect it, compare its metrics to newer versions, and reactivate it quickly if a rollback is needed. Deleting (option B) would permanently remove the version, making compliance review and rollback impossible and violating both requirements. Unregistering (option D) is not a standard Azure ML operation; deletion is the removal action. Archiving the training dataset (option A) is unrelated to the model version's deployability. Archiving creates a soft-block on deployment while preserving the full artifact history. Microsoft Learn Reference Topic: Manage model versions in the Azure Machine Learning registry -- Archive and lifecycle management