AI0-001 AI Concepts and Techniques Practice Question
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
CompTIA often tests the misconception that a larger context window or fine-tuning is the only way to handle dynamic data, when in fact RAG is the scalable, cost-effective 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. By indexing the documents in a vector store and retrieving relevant chunks at query time, RAG provides up-to-date, contextually accurate answers while keeping the underlying LLM static, which avoids the cost and complexity of monthly 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.
- ✗
Use a larger foundation model with a longer context window and paste all documents into each prompt
Why it's wrong here
Pasting all documents into every prompt consumes the context window and cannot scale as the corpus grows, and it still requires the model to re-read unchanged text each call. It is tempting because long-context prompting suits small, static document sets where retrieval infrastructure is unnecessary.
- ✓
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 to the model as context, so monthly document updates only require re-indexing, not retraining. This satisfies the constraint that the team cannot afford to retrain a model each time.
- ✗
Fine-tune a base LLM on the policy documents monthly
Why it's wrong here
Fine-tuning bakes document content into model weights, so monthly policy changes force repeated retraining and cost. Retrieval-augmented generation instead stores documents in a vector index and supplies relevant passages at query time, letting the team refresh content by re-indexing rather than retraining the model.
- ✗
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 it still cannot guarantee factual recall of specific policy text. Retrieval-augmented generation keeps the LLM frozen and retrieves relevant passages from a vector index at query time, so monthly document updates require only re-indexing.
About these practice questions
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.