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AI-102 Implement generative AI solutions Practice Question

Exhibit

Refer to the exhibit.
{
  "role": "system",
  "content": "You are a helpful assistant. Use the following sources to answer questions: [source1.pdf, source2.pdf]. If you cannot find the answer, say 'I don't know'."
}

You have deployed a chatbot using Azure OpenAI with a system message as shown. The chatbot sometimes provides incorrect answers that are not supported by the sources. What is the most likely cause?

⚠ Common exam trap

Many candidates assume a system message is sufficient to enforce factual accuracy, confusing instruction-following with grounded generation, and overlook the need for retrieval-augmented generation or explicit source constraints.

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 does not guarantee grounding; the model may still hallucinate.

A system message in Azure OpenAI provides instructions and context but does not enforce factual grounding. The model can still generate responses that are not supported by the provided sources (hallucination), especially if the system message is not explicitly designed to restrict the model to only use the given data. Grounding requires additional techniques like retrieval-augmented generation (RAG) or explicit constraints in the prompt.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Content filters are incorrectly configured, allowing harmful content.
Why it's wrong here
Incorrect answers are not necessarily harmful.
✗
The data ingestion pipeline has errors, so the sources are not available.
Why it's wrong here
If sources were unavailable, the model would likely say 'I don't know'.
✗
The temperature parameter is too low, causing repetitive answers.
Why it's wrong here
Low temperature reduces randomness, not incorrectness.
✓
The system message does not guarantee grounding; the model may still hallucinate.
Why this is correct
System messages are guidelines, not strict constraints, so the model may generate unsupported answers.

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AI-102 Implement generative AI solutions Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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