Question
A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
The team needs to introduce a new version of a model to production without disrupting existing users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?
- A. Deploy the model to a batch endpoint.
- B. Split traffic between deployments. Correct
- C. Replace the existing endpoint.
- D. Route all traffic to the new deployment.
Explanation
Online endpoints support several deployments behind one endpoint, so you can add the new version as a second deployment and send it a small share of live traffic (or mirrored traffic) while the current deployment keeps serving most users, then shift traffic gradually once it is validated. This is the documented safe rollout (blue-green) approach. Replacing the endpoint or routing all traffic to the new deployment removes the validation step and risks disruption, and a batch endpoint does not serve the real-time users.
