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LLM FundamentalsmediumMultiple SelectObjective-mapped

1Z0-1127-25 LLM Fundamentals Practice Question

An enterprise is deploying an LLM application on OCI and must minimize hallucinations. Which TWO strategies should they implement? (Choose two.)

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

Apply prompt engineering techniques such as asking the model to cite sources

RAG grounds the model in retrieved documents, and prompt engineering (e.g., asking the model to cite sources) can reduce hallucinations. Fine-tuning on factual data helps but may not eliminate hallucinations entirely. Increasing temperature increases randomness, which can worsen hallucinations. Using a smaller model typically reduces capability, making hallucinations more likely.

Answer analysis

Option-by-option breakdown

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

  • Apply prompt engineering techniques such as asking the model to cite sources

    Why this is correct

    Prompt engineering can guide the model to rely on provided context and cite sources, reducing hallucinations.

  • Increase the temperature parameter to encourage more diverse outputs

    Why it's wrong here

    Higher temperature increases randomness, which can increase hallucinations.

  • Use a smaller model to reduce complexity

    Why it's wrong here

    Smaller models generally have less capacity and may hallucinate more, not less.

  • Fine-tune the model on a dataset of factual question-answer pairs

    Why it's wrong here

    While fine-tuning can improve factual accuracy, it does not guarantee elimination of hallucinations and may not be as effective as RAG for dynamic knowledge.

  • Implement Retrieval-Augmented Generation (RAG) with a curated knowledge base

    Why this is correct

    RAG provides factual context, reducing the likelihood of the model generating incorrect information.

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