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AIF-C01 Practice Question: Build a customer service chatbot that answers…
A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?
⚠ Common exam trap
The AWS AI Practitioner exam often tests the misconception that fine-tuning is the only way to adapt a model to new data. The trap here is that RAG provides a cheaper, faster, and more maintainable solution for frequently updated knowledge bases 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 (RAG) with the policy documents indexed in a vector store
Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions based on the latest policy documents without retraining the model. RAG retrieves relevant document chunks from a vector store at inference time and injects them into the prompt, enabling the model to ground its responses in the most current information. This avoids the cost and latency of monthly fine-tuning or retraining, and it scales efficiently as documents are updated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger foundation model with a longer context window and paste all documents into each prompt
Why it's wrong here
Pasting every policy document into each prompt exceeds context limits, scales cost with document volume, and still cannot guarantee retrieval of the relevant passage. It is tempting because long context windows suit small, static document sets, and would be correct when the corpus is tiny and rarely updated.
- ✗
Train a custom model from scratch on the policy documents each month
Why it's wrong here
Monthly retraining from scratch is prohibitively expensive and slow, and cannot keep pace with document changes. It is tempting because fine-tuning on domain text genuinely improves task accuracy, but that suits stable corpora, not monthly updates. Retrieval-augmented generation indexes documents externally, so updates need no retraining.
- ✗
Fine-tune a base LLM on the policy documents monthly
Why it's wrong here
Fine-tuning bakes policy content into model weights, so monthly document updates force repeated retraining and cannot cite current sources. It is tempting because fine-tuning genuinely adapts tone and task format, and would be correct for teaching a model a stable style or domain vocabulary rather than frequently changing facts.
- ✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
Why this is correct
RAG retrieves relevant passages from the vector store at query time and supplies them as context, so monthly document updates only require re-indexing, not retraining. This satisfies the constraint that the team cannot afford to retrain the model each time.
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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.