Before releasing a RAG-based support-chat model, a team runs Azure AI Foundry's built-in evaluation tools to score, without a human reviewing every response by hand, how well each generated answer stays faithful to the retrieved source documents and how directly it addresses the user's original question. (Select TWO.) Which two evaluators would give the team these two signals?
Select all correct answers, then click Submit.
Short Explanation
You need two automatic quality checks: groundedness to see if the answer sticks to the retrieved documents, and relevance to see if it actually answers the user's question. Budget alerts and storage latency don't measure answer quality.
Full Explanation
The correct evaluators are groundedness and relevance. Groundedness scores whether the claims made in a generated answer are actually supported by the retrieved source documents, directly measuring faithfulness to the source material, while relevance scores how directly the answer addresses what the user actually asked, together giving the team both signals they need without manual review of every response. Azure Cost Management budget alerts are incorrect because budget alerts track spending against a threshold; they say nothing about whether an answer's content is accurate or on-topic. The network latency of the storage account hosting the documents is incorrect because storage account network latency is an infrastructure performance metric describing how fast documents are retrieved, not whether the retrieved content was used faithfully or whether the resulting answer addresses the question. Both distractors describe operational or financial monitoring rather than content-quality evaluation, so neither can substitute for the groundedness and relevance evaluators that Foundry provides specifically to automate answer-quality scoring in a RAG pipeline.