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

Explanation:
Microsoft ' s observability guidance for Azure AI Foundry applications describes a three-stage activation sequence. First, enable Tracing in the Microsoft Foundry project settings before deployment - Tracing instruments the application ' s LLM calls with OpenTelemetry-compatible spans that capture timing data for each step in the flow. Second, deploy the application to a production endpoint so that real traffic flows through the instrumented code path - without actual production traffic, there is no latency data to observe.
Third, configure Azure Monitor and Application Insights to receive, aggregate, and visualize the telemetry emitted by Tracing. Azure Monitor ' s metrics explorer and Application Insights ' performance views display p50, p95, and p99 latency distributions over time, enabling the team to identify latency regressions and set alert thresholds. This sequence - instrument, deploy, visualize - is the canonical Microsoft path to production AI performance observability.
Microsoft Learn Reference Topic: Monitor generative AI applications with Azure Monitor and Microsoft Foundry Tracing

Explanation:
A system requesting predictions synchronously during customer interactions needs sub-second responses, while a system submitting files with millions of records can tolerate minutes of processing time. For real-time synchronous serving, a Managed Online Endpoint provisions a persistent always-on container behind an HTTPS REST endpoint that returns predictions within milliseconds. For large-batch asynchronous scoring, a Batch Endpoint accepts a data asset input, distributes scoring across a compute cluster, and writes results back to storage. Online endpoints support auto-scaling based on request volume and traffic splitting. Batch endpoints are invoked on-demand or on a schedule, automatically provisioning and de-provisioning compute, keeping costs low for intermittent large jobs. Each deployment type is purpose-built for its usage pattern and should not be swapped.
Microsoft Learn Reference Topic: Deploy and score models with managed online endpoints and batch endpoints - Azure Machine Learning

A managed online endpoint can host multiple named deployments simultaneously, and you control what percentage of incoming traffic each deployment receives. With traffic splitting, you route a small percentage of live traffic to the new deployment while the majority continues to the proven existing model. You then monitor error rates, latency, and output quality for both deployments in real time under genuine production load. If the new model underperforms, you instantly route traffic back - no downtime, no user disruption. If it outperforms, you gradually increase its traffic share to 100%. Updating the registry version (option B) does not affect running deployments. A staging endpoint (option C) does not validate under real production load.
An evaluation script (option D) is a pre-deployment step. Traffic splitting is the blue/green and canary deployment pattern that Microsoft recommends for safe production rollouts.
Microsoft Learn Reference Topic: Perform safe rollout of new model deployments using traffic splitting - Azure Machine Learning

During experimentation, a data science team produces trained model artifacts they want to share with the MLOps team for deployment, with the governance team for compliance, and with the QA team for testing.
Registering the model in the Azure ML model registry gives each of these teams access to the versioned, immutable artifact through a single reference. Option B (Component) packages a reusable pipeline step, not the model output itself. Option C (Environment) captures the Python runtime, not the trained artifact. Option D (Pipeline) defines the orchestration workflow, not the resulting model. In Azure ML, the model registry is the governance store for trained model artifacts, recording who created it, when, from what data and code, and which metrics it achieved - making it the asset to register for reuse and governance across projects.
Microsoft Learn Reference Topic: Register and manage models in Azure Machine Learning - Model registry