AIF-C01 Fundamentals of Generative AI Practice Question
A healthcare startup is building a patient-education chatbot. They want the model to answer only using an approved set of clinical guideline documents, and they must be able to update those documents weekly without retraining any model. They plan to use Amazon Bedrock. Which approach best meets these requirements?
⚠ Common exam trap
The trap here is treating fine-tuning as the default way to add new knowledge, when retrieval is the mechanism that supports frequent updates without retraining.
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
✓
Use Retrieval Augmented Generation by storing the guidelines in a vector store and retrieving relevant passages at inference time.
Retrieval Augmented Generation separates knowledge from model weights. The approved guidelines live in a vector store that can be updated weekly, and relevant passages are retrieved and passed to the model as context. This grounds answers in approved content and avoids the cost and delay of fine-tuning for every content change.
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 a foundation model in Amazon Bedrock each week on the updated guideline documents.
Why it's wrong here
Fine-tuning changes model weights and is comparatively slow, costly, and operationally heavy for weekly updates. It also does not inherently constrain the model to answer only from those documents at inference time. This approach conflicts with the requirement to refresh content easily without retraining.
- ✗
Increase the model's context window by raising the maxTokens parameter for every request.
Why it's wrong here
maxTokens caps the number of tokens generated in the response; it does not expand the input context window or inject the guidelines. Even if the context window were larger, the model still needs the documents supplied. This option misidentifies a generation-length parameter as a mechanism for knowledge grounding.
- ✗
Create a separate Bedrock agent for each clinical guideline document and route questions by keyword.
Why it's wrong here
Splitting content across many agents and routing by keyword adds brittle logic and does not scale as guidelines change weekly. It also does not guarantee the model answers only from approved content. A single retrieval layer over a maintained knowledge base is simpler and more reliable than per-document agents with keyword routing.
- ✓
Use Retrieval Augmented Generation by storing the guidelines in a vector store and retrieving relevant passages at inference time.
Why this is correct
Retrieval Augmented Generation retrieves relevant passages from an external knowledge base and supplies them to the model as context. Updating the vector store with new guideline documents refreshes the knowledge without any retraining, and grounding responses in the retrieved text improves adherence to the approved source material.
Go deeper
Related to this question
About these practice questions
This AIF-C01 question is part of Courseiva's 862-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.