1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A financial institution uses an LLM for generating investment advice. They are concerned about hallucinations. Which method is most effective?
⚠ Common exam trap
Oracle often tests the misconception that simply fine-tuning or scaling a model can fix hallucinations, when in fact grounding via retrieval (RAG) is the most effective technique for factual accuracy in domain-specific applications.
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
✓
Use RAG with a verified corpus of regulations and reports.
Retrieval-Augmented Generation (RAG) grounds the LLM's output in a verified, external knowledge base (e.g., regulations and reports). By retrieving relevant documents at inference time, RAG reduces the model's reliance on its parametric memory, directly mitigating hallucinations in high-stakes domains like financial advice.
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 on general financial data.
Why it's wrong here
Incorrect: Fine-tuning may still produce hallucinations.
- ✓
Use RAG with a verified corpus of regulations and reports.
Why this is correct
Correct: Grounding in trusted data reduces hallucinations.
- ✗
Increase the temperature to get more creative responses.
Why it's wrong here
Incorrect: Higher temperature increases hallucination risk.
- ✗
Use a larger model to improve accuracy.
Why it's wrong here
Incorrect: Larger models can still hallucinate.
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