A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint. The endpoint returns errors after the new deployment is released. You need to restore the service as quickly as possible. What should you do first?

Speed of recovery is the central requirement. Rolling back traffic to the previous deployment is the fastest possible action: because the previous deployment still exists on the same endpoint, you simply update the traffic weights --- setting the old deployment to 100% and the new deployment to 0% --- using a single Azure ML CLI command or SDK call that completes in seconds without reprovisioning any compute. Deleting and redeploying (option B) requires tearing down the endpoint, waiting for deprovisioning, recreating it, re- deploying the model, and waiting for containers to start --- potentially 10 to 30 minutes. Changing authentication type (option C) does not affect application errors caused by a bad model. Increasing compute size (option D) does not fix model logic errors. The entire reason Azure ML supports multiple concurrent deployments with traffic splitting is precisely to enable this instant rollback pattern. Microsoft Learn Reference Topic: Roll back deployments on managed online endpoints -- Azure Machine Learning safe deployment practices