AI0-001 AI Concepts and Foundations Practice Question
A support team wants an AI assistant that can answer employee questions by retrieving passages from the company's internal policy documents and generating a response grounded in those passages. The documents change weekly. Which approach should the team implement?
⚠ Common exam trap
The trap here is assuming that any use of company documents requires fine-tuning, when retrieval is the lighter and more current option.
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
✓
Retrieval-augmented generation
Retrieval-augmented generation pairs a document retriever with a generative model, so the assistant answers from the latest policy passages instead of relying on memorized weights. This keeps responses current as documents change weekly, supports citation, and reduces hallucination. Fine-tuning, training from scratch, and ungrounded prompting all fail to keep pace with changing content or provide grounding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrieval-augmented generation
Why this is correct
Retrieval-augmented generation combines a retrieval component that fetches relevant passages from an external knowledge store with a generative model that composes an answer from them. Because the policy documents are updated weekly, retrieval keeps responses current without retraining the model, and grounding reduces fabrication.
- ✗
Training a model from scratch on the policy corpus
Why it's wrong here
Training from scratch demands enormous data and compute and would still freeze knowledge at the training date. For a narrow question-answering task over a small, changing document set, this approach is wasteful and would not keep pace with weekly edits.
- ✗
Fine-tuning the base model on all policy documents
Why it's wrong here
Fine-tuning bakes knowledge into model weights and requires a new training run for every document change. Weekly updates would make this costly and slow, and the model could still hallucinate details. Retrieval-augmented generation is better suited to frequently changing content.
- ✗
Prompting the model with only the user's question
Why it's wrong here
A bare prompt gives the model no access to internal policy text, so answers would come from general training data and may be wrong or invented. Without retrieved context, the assistant cannot reliably cite current company policy.
About these practice questions
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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 CompTIA exam blueprint
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.