AI-103: Developing AI Apps and Agents on AzureDemo

By Microsoft · Question Mode

QuestionQ46

Plan and manage an Azure AI solution

You have a Microsoft Foundry project that uses Azure AI Search to ground an agent in internal documentation.

After a recent content update, users report that the agent's answers have become less accurate.

You need to determine whether the retrieved content is negatively affecting the model's generated responses.

Which observability signal should you review?

  • A indexer status and failure history
  • B latency breakdown traces
  • C prediction drift metrics
  • D groundedness evaluation metrics
Explanation

Groundedness evaluation metrics in Microsoft Foundry observability measure whether a generated response is actually supported by the content retrieved for the agent, so a drop in groundedness after a content update points directly at the retrieved content as the source of degraded answer quality. Indexer status and failure history only indicate whether ingestion succeeded, latency breakdown traces show performance timing, and prediction drift metrics track changes in model output distribution over time — none of these directly reveal whether the retrieved content is undermining response accuracy.

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