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 teaches style and conversational patterns from examples; it does not supply authoritative specification data at inference time, so fabricated values persist. Fine-tuning suits adapting tone or task format, whereas grounding answers in retrieved source documents is what constrains hallucination here.
- ✓
Integrate a Retrieval Augmented Generation (RAG) system with the product catalog.
Why this is correct
Integrating RAG grounds responses in retrieved product-catalogue content, so the model conditions on factual specifications rather than relying solely on parametric memory. This directly targets the hallucination source by supplying authoritative context at inference time, satisfying the requirement to reduce fabricated product details without retraining the foundation model.
- ✗
Use more detailed prompts with explicit instructions to avoid speculation.
Why it's wrong here
Prompt instructions asking the model not to speculate do not ground responses in authoritative product data, so fabrication can persist. It is tempting because prompt engineering is quick and low-cost, and would be correct for steering tone, format or scope rather than factual accuracy against a source of truth.
- ✗
Increase the temperature parameter to make outputs more conservative.
Why it's wrong here
Raising temperature increases sampling randomness, which amplifies hallucination rather than suppressing it; conservative output requires lowering temperature toward zero. The parameter is genuinely useful for creative or brainstorming tasks where varied phrasing is wanted, but it cannot ground responses in verified product specifications.
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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.