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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

A company uses OCI Generative AI to power a chatbot for customer support. They notice that the model's responses sometimes contain factual inaccuracies. Which strategy would best reduce 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

✓

Implementing Retrieval-Augmented Generation (RAG).

Retrieval-Augmented Generation (RAG) grounds the model's responses in retrieved factual information, directly reducing hallucination. Increasing temperature increases randomness, fine-tuning on a larger corpus may not fix factual accuracy, and reducing max tokens does not affect correctness.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Implementing Retrieval-Augmented Generation (RAG).
Why this is correct
RAG retrieves relevant facts from a knowledge base, grounding the output and reducing hallucination.
✗
Increasing the temperature parameter.
Why it's wrong here
Higher temperature increases randomness, which can worsen hallucination.
✗
Reducing the max token limit.
Why it's wrong here
Max tokens limit output length but does not affect factual accuracy.
✗
Fine-tuning the model on a larger general corpus.
Why it's wrong here
Fine-tuning on general data does not specifically address factual inaccuracies; it may even introduce noise.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

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