Microsoft Azure AI Engineer Associate AI-102 Practice Question
You have created a retrieval-augmented generation (RAG) prompt flow in Azure AI Studio. Before deploying the flow, you run an automatic evaluation run from the Evaluate pane and enable all built-in metrics. You want to determine whether the model is hallucinating or inventing facts that are not found in the documents returned by the retriever. Which metric should you focus on, and what does a consistently low score for that metric most likely indicate?
Groundedness - the answer contains content that is not supported by the retrieved passages.
Semantic similarity - the answer's wording differs from a predefined reference answer.
Relevance - the answer does not correspond to the intent of the user question.
Fluency - the answer contains spelling or grammatical mistakes.
The groundedness metric compares each factual statement in the model's answer with the passages that the retriever supplied. If the text cannot be traced back to the provided context, the groundedness score drops. Therefore, a low groundedness score signals that portions of the answer are unsupported by the retrieved documents-an indication of hallucination. Fluency concerns grammatical quality, relevance measures how well the answer addresses the user's question, and semantic similarity compares the answer to a reference answer; none of these directly measure whether the answer is backed by the source passages.
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