QuestionQ132
Implement generative AI quality assurance and observabilityAn organization operates a customer-facing generative AI application built using Microsoft Foundry. The application uses multiple prompts connected to multiple workflows to generate production responses.
The application occasionally produces incomplete responses. Although the model call succeeds, the final message sometimes ends early.
The issue cannot be reproduced consistently in development.
You need to determine where and why response generation is ending early in production.
Which approach should you use?
- A Run a pre-release evaluation workflow to score groundedness and relevance on a test dataset.
- B Enable tracing and logging so that each workflow can be inspected.
- C Increase max_tokens and temperature to reduce the chance of early termination.
- D Replace the deployed model with a smaller model to reduce variability across responses.
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