AI-102 Implement generative AI solutions Practice Question
You are deploying a chatbot using Azure OpenAI Service with a custom dataset indexed in Azure AI Search. Users report that the chatbot frequently responds with 'I don't know' for questions that the dataset should cover. What is the most likely cause?
⚠ Common exam trap
Many exam-takers confuse the confidence threshold in retrieval with the temperature parameter in generation, assuming a high temperature causes the model to refuse answers, when in fact temperature controls creativity, not retrieval filtering.
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 confidence threshold in the retrieval configuration is set too low, filtering out relevant chunks.
When the confidence threshold is set too low, Azure AI Search filters out retrieved chunks that don't meet the minimum confidence score, even if those chunks are relevant. This causes the chatbot to respond with 'I don't know' because no sufficiently confident context is passed to the Azure OpenAI model for answer generation. The issue is specifically in the retrieval configuration, not in the model's generation parameters.
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 search scope is limited to a small number of documents.
Why it's wrong here
Limited search scope may reduce relevant results, but the symptom is 'I don't know' for questions that should be covered; confidence threshold is more direct.
- ✓
The confidence threshold in the retrieval configuration is set too low, filtering out relevant chunks.
Why this is correct
Low confidence threshold causes relevant chunks to be excluded, leading to 'I don't know' responses.
- ✗
The temperature setting in the model deployment is set too high.
Why it's wrong here
High temperature increases randomness, not refusal to answer.
- ✗
The chunk size in the index is too large, causing irrelevant chunks to be retrieved.
Why it's wrong here
Large chunk size may cause irrelevant retrieval, but the symptom is 'I don't know' for valid questions, not irrelevant answers.
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