Microsoft Azure AI Engineer Associate AI-102 Practice Question
You manage a custom question answering project in Azure Language Studio named ContosoFAQ. The project is already deployed to the production endpoint that serves a public website. Before you add several new question-and-answer pairs, you must validate the changes without affecting users. According to Microsoft's recommended workflow, what should you do first?
Export the project to a new Azure AI Language resource and test it there.
Overwrite the production deployment with the newly trained model and roll back if issues appear.
Send generateAnswer requests to the production endpoint with an isTest=true parameter.
Query the built-in test deployment by calling the prediction REST API with deploymentName=test.
Every custom question answering project automatically provides a test deployment. Whenever you edit or retrain the project in Language Studio, the changes are available through the test endpoint identified by deploymentName=test. Sending requests to that endpoint lets you preview answers while the production deployment continues to serve live traffic. Overwriting production or exporting to a new resource would risk downtime or complicate management, and an isTest flag is only available in the retired QnA Maker service.
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How does the prediction REST API with deploymentName=test work?
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Why can't isTest=true be used for validating changes in Azure Language Studio?
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Microsoft Azure AI Engineer Associate AI-102
Implement natural language processing solutions
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