1Z0-1127-25 LLM Fundamentals Practice Question
An enterprise is building a document Q&A application with OCI Generative AI. They want to minimize hallucinations. Which TWO techniques 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
✓
Fine-tune the model on domain-specific data
RAG grounds answers in retrieved documents, and fine-tuning on domain-specific data reduces errors. Increasing context window alone does not reduce hallucinations; greedy decoding reduces creativity but not factual errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fine-tune the model on domain-specific data
Why this is correct
Fine-tuning improves factual accuracy for the domain, reducing hallucinations.
- ✗
Increase the model's context window to its maximum
Why it's wrong here
A longer context window does not directly reduce hallucinations; it can even introduce noise.
- ✓
Use Retrieval-Augmented Generation (RAG)
Why this is correct
RAG provides factual context from documents, reducing hallucinations.
- ✗
Use a higher temperature in sampling
Why it's wrong here
Higher temperature increases randomness, worsening hallucinations.
- ✗
Apply greedy decoding
Why it's wrong here
Greedy decoding reduces randomness but does not add factual grounding.
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