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AIF-C01 Practice Question: Using an LLM to generate customer support…

A company is using an LLM to generate customer support responses. They want to reduce hallucinations and improve the accuracy of the responses. Which TWO approaches are most effective? (Select 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 Bedrock Guardrails with contextual grounding check

RAG grounds the model in retrieved facts, while Bedrock Guardrails with contextual grounding check validates responses against sources. Both are proven techniques to reduce hallucinations.

Answer analysis

Option-by-option breakdown

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

  • Increase the temperature parameter to 1.0

    Why it's wrong here

    Higher temperature increases randomness and can worsen hallucinations.

  • Use a smaller model to reduce complexity

    Why it's wrong here

    Smaller models may have less knowledge and can still hallucinate; model size is not a direct solution for hallucination.

  • Remove all system prompts

    Why it's wrong here

    System prompts guide model behavior; removing them may reduce control and increase hallucinations.

  • Apply Bedrock Guardrails with contextual grounding check

    Why this is correct

    Contextual grounding check verifies that the model's response is supported by the retrieved sources, filtering out unsupported claims.

  • Use Retrieval-Augmented Generation (RAG) to retrieve relevant documents

    Why this is correct

    RAG provides factual context at inference time, reducing the model's reliance on internal knowledge and thus reducing hallucinations.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.