Microsoft Azure AI Engineer Associate AI-102 Practice Question
You have created a custom extraction model (v1) by training on 12 unlabeled sample documents in Azure AI Document Intelligence Studio. A production API uses the model through the alias "invoices-prod", which you published after the first training. During user acceptance testing you discover several fields are missing, so you upload five additional representative documents and retrain the model, which produces a new version (v2).
You must evaluate v2 without disrupting the application that is still calling alias "invoices-prod". What should you do next?
Republish version v2 to the existing alias "invoices-prod" and roll back if problems occur.
Publish version v2 by assigning it a new alias such as "invoices-staging", then call that alias during testing.
Delete the alias "invoices-prod" from version v1, then publish version v2 with the same alias.
Rename the model ID of version v2 and update the production application to call the new ID when testing is complete.
Publishing attaches an alias to a specific model version. Only one version can be published to an alias at a time, so republishing v2 to "invoices-prod" would immediately redirect production traffic. Instead, publish the new version under a different alias-commonly "staging" or any name you choose. The existing alias continues to resolve to v1, while you can test v2 by using the new alias or the version-specific model ID. Deleting the old alias or renaming the model is unnecessary and would break the running workload.
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Microsoft Azure AI Engineer Associate AI-102
Implement knowledge mining and information extraction solutions
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