AI-103: Developing AI Apps and Agents on AzureDemo

By Microsoft · Question Mode

QuestionQ69

Implement generative AI and agentic solutions

You have a Microsoft Foundry project containing a Retrieval Augmented Generation (RAG) chat solution used by customer support agents.

You are adding an automated pre-production evaluation step to a CI/CD pipeline named Pipeline1. The evaluation will run against a labeled test dataset containing support questions along with the expected grounding context.

You need to ensure that Pipeline1 fails if unsupported content or a retrieval mismatch exceeds a defined threshold, where:

  • Responses include claims that are not supported by the retrieved source content.
  • The retrieved source content does not align with the labeled expected context.

Which two built-in evaluators should you use in Pipeline1? (Each correct answer presents part of the solution.)

Note: Each correct selection is worth one point.

Choose two
  • A Retrieval
  • B Fluency
  • C Coherence
  • D Groundedness
  • E Response Completeness
Explanation

Azure AI Foundry's built-in RAG evaluators include Groundedness and Retrieval. The Groundedness evaluator measures whether a generated response's claims are fully supported by the retrieved source content, catching hallucinated or unsupported statements. The Retrieval evaluator assesses the quality and relevance of the retrieved chunks relative to the query, allowing detection of cases where retrieved content does not align with the expected labeled context. Fluency and Coherence measure the linguistic quality of the response text rather than factual grounding or retrieval accuracy, and Response Completeness measures coverage relative to a ground-truth answer rather than retrieval alignment, so these do not satisfy the stated requirements.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!