1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A company fine-tunes an LLM on internal support tickets. After deployment, the model hallucinates company-specific product names. What is the most effective mitigation?
⚠ Common exam trap
Oracle often tests the misconception that fine-tuning alone can fix factual accuracy for domain-specific entities, when in reality RAG is required to ground outputs in a verifiable external knowledge source.
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
✓
Implement RAG with a verified product database
RAG (Retrieval-Augmented Generation) grounds the LLM's output in a verified product database, providing factual context that prevents hallucination of company-specific product names. Unlike fine-tuning, which only adjusts model weights and can still produce plausible but incorrect names, RAG retrieves exact records at inference time, ensuring accuracy for proprietary terminology.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a smaller model to reduce hallucination risk
Why it's wrong here
Smaller models often hallucinate more due to limited capacity.
- ✗
Use prompt engineering to remind the model to be accurate
Why it's wrong here
Prompts are not robust against ingrained hallucinations.
- ✓
Implement RAG with a verified product database
Why this is correct
RAG provides factual grounding, reducing hallucinations.
- ✗
Fine-tune further with more ticket data
Why it's wrong here
More data may not correct hallucinations if the model has learned wrong patterns.
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