1Z0-1127-25 LLM Fundamentals Practice Question
A team wants to reduce hallucinations in their LLM-powered question-answering system. Which TWO techniques are most effective?
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
✓
Implementing RAG to retrieve relevant documents
RAG provides factual grounding, and reducing temperature makes outputs more deterministic, reducing fabricated details.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implementing RAG to retrieve relevant documents
Why this is correct
RAG grounds answers in retrieved facts.
- ✗
Switching to a smaller model
Why it's wrong here
Smaller models may hallucinate more.
- ✓
Using a lower temperature (e.g., 0) for more deterministic outputs
Why this is correct
Lower temperature reduces randomness, decreasing hallucinations.
- ✗
Using a larger context window
Why it's wrong here
Larger context window does not directly reduce hallucinations.
- ✗
Increasing the temperature to 1.5
Why it's wrong here
Higher temperature increases randomness and hallucinations.
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