Implement generative AI solutions →hardMultiple Choice

AI-102 Implement generative AI solutions Practice Question

Exhibit

Refer to the exhibit.
{
  "role": "system",
  "content": "You are an AI assistant that helps users find information. When you don't know the answer, say 'I don't know' and do not make up information."
}

You have configured a system message for an Azure OpenAI chat completion deployment as shown in the exhibit. Users are reporting that the assistant sometimes refuses to answer questions that are clearly within the scope of the provided data. What is the most likely issue?

⚠ Common exam trap

Microsoft often tests the misconception that refusal issues are caused by missing data instructions or high temperature, when in fact the root cause is the system message's overly cautious phrasing that induces false-negative refusals.

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 system message encourages the model to err on the side of caution, leading to false-negative refusals.

The system message likely contains overly cautious language (e.g., 'only answer if you are certain' or 'do not speculate'), which causes the model to refuse answering even when the data clearly supports the response. This is a known behavior in Azure OpenAI chat completions where the system message's tone and constraints directly influence refusal rates, leading to false-negative refusals.

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 system message encourages the model to err on the side of caution, leading to false-negative refusals.
Why this is correct
Overly defensive system message wording instructs the model to decline when uncertain, so legitimate in-scope questions fall below its confidence threshold and trigger refusals. Softening that instruction, while keeping grounding constraints, restores answers without permitting out-of-scope responses.
✗
The system message explicitly prohibits making up information, which is correct behavior.
Why it's wrong here
Instructing the model not to fabricate is standard grounding practice and prevents hallucination; it does not cause refusals. The actual fault is a system message that is too restrictive, such as demanding citations or forbidding answers when context is incomplete.
✗
The system message does not include instructions to use the provided data.
Why it's wrong here
A system message without grounding instructions leaves the model relying on its own priors, so it declines in-scope questions. It is tempting because system messages are the correct place for behavioural constraints, but the failing requirement here is directing the model to answer from the supplied data.
✗
The temperature parameter is set too high, causing the model to hallucinate.
Why it's wrong here
Temperature controls randomness of token selection, so a high value yields varied or creative wording, not refusals. The reported behaviour is the model declining to answer, which stems from overly restrictive system-message instructions rather than sampling parameters.

Go deeper

AI-102 Implement generative AI solutions Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question