1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A healthcare startup is building an AI assistant to help doctors draft clinical notes from patient-physician conversations. They have a large language model that is fine-tuned on medical data. During testing, they notice the model occasionally generates plausible-sounding but incorrect medical recommendations. The startup wants to deploy the assistant to assist doctors, not replace them. They have the following options: (A) Deploy the model as-is and rely on doctors to catch errors, (B) Add a disclaimer that the model may make mistakes, (C) Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base before presenting to doctors, (D) Reduce the model's temperature to 0 to ensure deterministic outputs. Which option best balances safety and utility?
⚠ Common exam trap
The Oracle OCI Generative AI certification tests understanding that temperature controls output randomness and not factual accuracy; reducing temperature to 0 does not prevent hallucination.
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 a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base.
Implementing a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base directly mitigates the risk of hallucinated medical recommendations while preserving the assistant's utility. This approach leverages retrieval-augmented generation (RAG) principles to ground the model's outputs in verified facts, ensuring safety without sacrificing the flexibility needed for drafting clinical notes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base.
Why this is correct
Fact-checking reduces hallucinations and ensures accuracy.
- ✗
Add a disclaimer that the model may make mistakes.
Why it's wrong here
Disclaimer does not reduce risk of incorrect advice.
- ✗
Deploy the model as-is and rely on doctors to catch errors.
Why it's wrong here
Doctors may miss errors; this is unsafe.
- ✗
Reduce the model's temperature to 0 to ensure deterministic outputs.
Why it's wrong here
Deterministic outputs can still be incorrect.
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