AI-102 Implement generative AI solutions Practice Question
You are implementing a RAG (Retrieval-Augmented Generation) solution using Azure AI Search and Azure OpenAI Service. The solution is returning answers that are not relevant to the user query. What is the most likely cause?
⚠ Common exam trap
Many exam-takers confuse the relevance score threshold with other parameters like max_tokens or chunk size, assuming that irrelevant answers stem from generation limits or indexing granularity rather than retrieval quality.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The relevance score threshold is set too low.
A low relevance score threshold in Azure AI Search allows documents with low semantic or vector similarity to be returned as results. When these poorly matched documents are passed to Azure OpenAI Service for answer generation, the model may produce answers that are not relevant to the user query, as the retrieved context is noisy or unrelated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The max_tokens parameter is set too high.
Why it's wrong here
Max_tokens doesn't affect relevance of retrieved documents.
- ✗
The chunk size is too small.
Why it's wrong here
Small chunks may break context but don't directly cause irrelevance.
- ✗
The index includes too many documents.
Why it's wrong here
Large index is fine if retrieval is accurate; threshold is the issue.
- ✓
The relevance score threshold is set too low.
Why this is correct
Low threshold includes less relevant documents, leading to poor responses.
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