1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A financial institution uses OCI GenAI to power a customer support chatbot. The compliance team requires that responses are strictly consistent with regulatory guidelines and approved responses. The company has a curated set of question-answer pairs that cover common scenarios. They want to ensure that the chatbot never deviates from these approved answers. The data science team is considering various approaches to enforce this consistency. Which approach is most effective?
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-tuning the model on the curated dataset of question-answer pairs.
Fine-tuning the model on the curated dataset of approved responses teaches the model to output similar responses for related questions, ensuring consistency. Option A is wrong because few-shot prompting may fail for unseen variations and does not guarantee strict adherence. Option C is wrong because using a large context window does not enforce specific content. Option D is wrong because setting a low temperature reduces randomness but does not guarantee the model will choose approved responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Few-shot prompting with three example responses in every query.
Why it's wrong here
Few-shot examples may not cover all variations, and the model may still generate unapproved content.
- ✓
Fine-tuning the model on the curated dataset of question-answer pairs.
Why this is correct
Fine-tuning adapts the model to mimic the approved responses, providing strong consistency.
- ✗
Using a large context window to include all regulatory guidelines in the prompt.
Why it's wrong here
Even with a large context, the model may not consistently follow the guidelines.
- ✗
Setting a low temperature (0.1) to make outputs deterministic.
Why it's wrong here
Low temperature reduces randomness but does not constrain content to approved responses.
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