AIF-C01 Fundamentals of Generative AI Practice Question
A company is using Amazon Bedrock to generate marketing copy. They want to ensure the model's responses are factually accurate and grounded in their proprietary knowledge base. Which feature should they use?
⚠ Common exam trap
A common misconception is that you must fine-tune or customize a model to incorporate proprietary knowledge. However, with Amazon Bedrock, RAG allows you to ground responses in your knowledge base without retraining, which is more cost-effective and keeps information current.
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
✓
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is the correct choice because it retrieves relevant documents from the company's proprietary knowledge base and provides them as context to the foundation model at inference time. This grounds the model's responses in factual, up-to-date information without modifying the underlying model weights, ensuring accuracy and reducing 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.
- ✗
Model customization
Why it's wrong here
Model customization alters a model's weights or adapters to change style and task behaviour; it does not attach a retrievable corpus, so outputs remain ungrounded in the proprietary knowledge base. Customisation suits teaching a model a consistent format or domain tone, not supplying authoritative facts at inference time.
- ✗
Fine-tuning
Why it's wrong here
Fine-tuning bakes training examples into model weights, which cannot cite or update proprietary sources and risks hallucinating stale facts. It suits adapting style or task format. Grounding in a knowledge base requires retrieval, so answers are generated from current, citable source documents at inference time.
- ✓
Retrieval Augmented Generation (RAG)
Why this is correct
Retrieval Augmented Generation queries the proprietary knowledge base and injects relevant retrieved passages into the prompt, grounding responses in the company's own content. This satisfies the factual-accuracy requirement by supplying authoritative context the base model lacks, reducing hallucination.
- ✗
Prompt engineering
Why it's wrong here
Prompt engineering shapes instructions and context within the token window; it cannot reliably ground responses in a large proprietary corpus, and accuracy degrades as content grows. It suits steering tone, format and reasoning style. Grounding requires retrieving relevant passages from the knowledge base and supplying them to the model.
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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.