1Z0-1127-25 LLM Fundamentals Practice Question
An enterprise is deploying an LLM application on OCI and must minimize hallucinations. Which TWO strategies should they implement? (Choose two.)
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
✓
Apply prompt engineering techniques such as asking the model to cite sources
RAG grounds the model in retrieved documents, and prompt engineering (e.g., asking the model to cite sources) can reduce hallucinations. Fine-tuning on factual data helps but may not eliminate hallucinations entirely. Increasing temperature increases randomness, which can worsen hallucinations. Using a smaller model typically reduces capability, making hallucinations more likely.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply prompt engineering techniques such as asking the model to cite sources
Why this is correct
Prompt engineering can guide the model to rely on provided context and cite sources, reducing hallucinations.
- ✗
Increase the temperature parameter to encourage more diverse outputs
Why it's wrong here
Higher temperature increases randomness, which can increase hallucinations.
- ✗
Use a smaller model to reduce complexity
Why it's wrong here
Smaller models generally have less capacity and may hallucinate more, not less.
- ✗
Fine-tune the model on a dataset of factual question-answer pairs
Why it's wrong here
While fine-tuning can improve factual accuracy, it does not guarantee elimination of hallucinations and may not be as effective as RAG for dynamic knowledge.
- ✓
Implement Retrieval-Augmented Generation (RAG) with a curated knowledge base
Why this is correct
RAG provides factual context, reducing the likelihood of the model generating incorrect information.
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