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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
AWS often tests the misconception that fine-tuning is the only way to incorporate new data, but the trap here is that candidates overlook RAG's ability to handle dynamic, frequently updated knowledge bases without retraining, which is a core AIF-C01 concept in the AI and ML Fundamentals domain.
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 underlying language model. By indexing the documents in a vector store and retrieving relevant chunks at query time, RAG provides up-to-date, grounded responses while keeping the base LLM static, which is both cost-effective and scalable for monthly document updates.
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
Fine-tuning bakes policy content into model weights, so each monthly document change demands another training run — precisely the retraining cost the team cannot afford. It is tempting because fine-tuning genuinely suits teaching tone, format, or domain style. Retrieval-augmented generation, by contrast, updates answers by refreshing the indexed documents alone.
- ✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
Why this is correct
Retrieval-Augmented Generation (RAG) avoids retraining by storing policy documents as vector embeddings in a vector store, then retrieving the most relevant chunks at query time to ground the language model’s response. This satisfies the constraint that documents are updated monthly, as only the vector index needs re-indexing, not the underlying model.
- ✗
Use a larger foundation model with a longer context window and paste all documents into each prompt
Why it's wrong here
Pasting all policy documents into each prompt exceeds the context window’s token limit as documents grow monthly, causing truncation and loss of answer accuracy. This approach is tempting because a larger context window can ingest entire documents for static, single-session queries, making it correct for a one-off knowledge base that does not change frequently.
- ✗
Train a custom model from scratch on the policy documents each month
Why it's wrong here
Training from scratch each month is prohibitively expensive and slow, and the resulting model still cannot cite current policy text without retraining. From-scratch training suits novel domains or architectures where no pretrained foundation model applies.
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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.