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Fundamentals of Generative AIhardMultiple ChoiceObjective-mapped

AIF-C01 Fundamentals of Generative AI Practice Question

An organization is using Amazon Bedrock to power a customer service chatbot. They notice that the chatbot occasionally generates hallucinated information about product specifications. Which strategy should be implemented to reduce hallucinations?

⚠ Common exam trap

The AIF-C01 exam often tests the misconception that prompt engineering or fine-tuning alone can solve hallucination problems, when in fact they lack the dynamic, verifiable grounding that RAG provides.

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

Integrate a Retrieval Augmented Generation (RAG) system with the product catalog.

Retrieval Augmented Generation (RAG) grounds the model's responses in authoritative, up-to-date product catalog data, directly reducing hallucinations by ensuring the chatbot references verified facts rather than relying solely on its parametric memory. This is the most effective strategy because it provides a retrieval-based factual foundation that fine-tuning or prompt engineering alone cannot guarantee.

Answer analysis

Option-by-option breakdown

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

  • Fine-tune the model on a dataset of product specification conversations.

    Why it's wrong here

    Fine-tuning might help but is expensive and may not cover all edge cases.

  • Integrate a Retrieval Augmented Generation (RAG) system with the product catalog.

    Why this is correct

    RAG provides up-to-date, factual context to the model, reducing hallucinations.

  • Use more detailed prompts with explicit instructions to avoid speculation.

    Why it's wrong here

    Prompt engineering helps but does not guarantee factual accuracy for specific details.

  • Increase the temperature parameter to make outputs more conservative.

    Why it's wrong here

    Higher temperature increases randomness, the opposite of what is needed.

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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.