mediumMultiple Choice
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
AWS often tests the misconception that fine-tuning or retraining is necessary for domain-specific knowledge, when in fact RAG provides a cost-effective, update-friendly alternative that avoids model modification.
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. By indexing the documents in a vector store and retrieving relevant chunks at inference time, the system can incorporate monthly updates simply by re-indexing the new documents, keeping the model static and avoiding costly retraining.
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 base LLM on the policy documents monthly
Why it's wrong here
Monthly fine-tuning cannot keep pace with document updates and still requires retraining, which the team cannot afford. Fine-tuning teaches a model a style or domain behaviour, so it suits adapting tone or task format on stable data — not injecting frequently changing facts that retrieval-augmented generation handles.
- ✗
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 any context window once the corpus grows, and the model still cannot cite or filter reliably. Long-context prompting suits a small, fixed set of documents queried occasionally, not a monthly-updated corpus needing retrieval-augmented generation.
- ✗
Train a custom model from scratch on the policy documents each month
Why it's wrong here
Training from scratch each month demands labelled data, compute and ML expertise the team lacks, and rebuilding monthly is impractical. From-scratch training suits novel domains or architectures where no pretrained model exists — not grounding answers in an existing, changing document set via retrieval-augmented generation.
- ✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
Why this is correct
RAG retrieves relevant passages from a vector store at query time and supplies them as context to the language model, so monthly document updates only require re-indexing rather than retraining. This satisfies the constraint that the team cannot afford model retraining each time policies change.
About these practice questions
One of 862 original AIF-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.