Question

Your team runs a production chat application on an Azure AI Foundry deployment of a GPT model. Compliance requires that any model behavior change be validated in a staging environment before it reaches production. During a quarterly review you discover that the production deployment's underlying model version silently changed, causing subtly different responses that were never tested. When you inspect the deployment, its version update policy is set to allow automatic upgrades. What should you change to ensure the production deployment stays on a validated model version until you explicitly approve an upgrade?

Accepted Answer

Set the deployment's version update policy to 'No auto update' so it stays pinned to the specific model version you selected

Answer

Set the deployment's version update policy to 'Once new default version is available' (auto-update to default)

Answer

Reduce the deployment's provisioned throughput units so upgrades cannot be applied during high load

Answer

Enable a content safety filter on the deployment to block responses from newer model versions