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 caps response length, not retrieval relevance; irrelevant answers stem from poor search results, not output truncation. Raising it is tempting when answers appear cut off mid-sentence, and it would be correct for generating longer completions from already-relevant context.
- ✗
The chunk size is too small.
Why it's wrong here
Smaller chunks fragment context, so retrieval returns passages lacking the surrounding detail the query needs, producing irrelevant grounding. Larger chunks are tempting because they preserve context, and would suit queries needing broad narrative passages rather than precise factual lookups.
- ✗
The index includes too many documents.
Why it's wrong here
Index volume alone does not cause irrelevance; ranking and semantic configuration determine which documents surface. Large indexes are tempting to blame when results drift, yet they are correct for broad corpora provided filtering, scoring profiles and vector search are tuned.
- ✓
The relevance score threshold is set too low.
Why this is correct
A low relevance score threshold lets weakly matching chunks pass into the prompt, so Azure OpenAI grounds its answer on marginal content and returns off-topic responses. Raising the threshold filters these poor matches, directly addressing the irrelevant-answer symptom described in the stem.
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