- A
Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base.
Fact-checking reduces hallucinations and ensures accuracy.
- B
Add a disclaimer that the model may make mistakes.
Why wrong: Disclaimer does not reduce risk of incorrect advice.
- C
Deploy the model as-is and rely on doctors to catch errors.
Why wrong: Doctors may miss errors; this is unsafe.
- D
Reduce the model's temperature to 0 to ensure deterministic outputs.
Why wrong: Deterministic outputs can still be incorrect.
Question 830 of 991
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Command / output trap
Deterministic outputs can still be incorrect.
Detailed technical explanation
How to think about this question
Under the hood, a fact-checking pipeline typically uses a vector database of trusted medical literature (e.g., PubMed, clinical guidelines) to retrieve relevant passages via embedding similarity, then passes them as context to the LLM for generation—a process known as retrieval-augmented generation (RAG). This not only reduces hallucinations but also provides traceable citations, which is critical in regulated healthcare environments. A subtle behavior is that even with RAG, the model may ignore retrieved context if the prompt is poorly structured, so careful prompt engineering is required to force the model to rely on the provided facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement a fact-checking pipeline that cross-references outputs with a trusted medical knowledge base. — Option A is correct because 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.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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