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Fundamentals of Large Language ModelsmediumMultiple ChoiceObjective-mapped

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

A company is deploying a large language model in a customer-facing chatbot. The model's responses must be both accurate and safe. Which combination of techniques should be employed?

⚠ Common exam trap

Oracle often tests the misconception that a single technique (like a system prompt or fine-tuning) can simultaneously guarantee both accuracy and safety, when in practice they require separate, complementary mechanisms.

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 retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.

RAG grounds the model's responses in a verified external knowledge base, reducing hallucinations and improving factual accuracy, while a content safety filter (e.g., a classifier or guardrail) actively blocks toxic or unsafe outputs before they reach the user. This combination addresses both accuracy and safety independently, unlike a single system prompt which is easily bypassed.

Answer analysis

Option-by-option breakdown

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

  • Use only a system prompt instructing the model to be accurate and safe.

    Why it's wrong here

    System prompts alone are often insufficient to guarantee safety and accuracy.

  • Use retrieval-augmented generation (RAG) for factual accuracy and a content safety filter for safe outputs.

    Why this is correct

    RAG improves accuracy; safety filter ensures safety.

  • Use a high temperature for creativity and a safety classifier for blocking toxic outputs.

    Why it's wrong here

    High temperature can reduce accuracy.

  • Fine-tune the model on all historical chat logs and use a high temperature.

    Why it's wrong here

    Historical chat logs may contain biases and safety issues.

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