AI0-001 Implementing AI Solutions Practice Question
A team is considering whether to fine-tune a base LLM or use RAG for a question-answering system over a large, static corpus of scientific papers. The answer must be highly accurate and grounded in the papers. Which approach is BEST and why?
⚠ Common exam trap
The trap is assuming fine-tuning is always better for domain specialization, when the question's emphasis on grounding in source documents points decisively to RAG.
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
✓
RAG because it retrieves and grounds answers in the source documents
RAG is the best fit because it retrieves relevant passages from the scientific corpus at query time and injects them into the LLM's context, grounding the answer in the actual source documents. This directly satisfies the requirement for high accuracy and traceability to the papers, and it avoids the cost and staleness issues of fine-tuning on a large static corpus. Fine-tuning changes model weights but does not guarantee the model will cite or stay faithful to specific documents.
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-tuning because it adapts the model to the scientific domain
Why it's wrong here
Fine-tuning bakes domain style into weights but cannot cite the papers, so answers cannot be grounded in specific sources as the stem demands. It is tempting because fine-tuning genuinely adapts tone and terminology to a domain, and would suit tasks needing stylistic specialisation rather than verifiable citation.
- ✗
Fine-tuning because it is faster at inference time
Why it's wrong here
Inference speed is not the stem's requirement; grounding and accuracy are. Fine-tuning can reduce prompt length, but it cannot retrieve or cite the corpus, so it fails the grounding criterion. It is tempting because fine-tuned models often do serve faster, which would matter for latency-sensitive deployments.
- ✓
RAG because it retrieves and grounds answers in the source documents
Why this is correct
RAG retrieves relevant passages from the static corpus and conditions generation on them, grounding answers in the source papers. Fine-tuning bakes knowledge into weights, which risks hallucination and staleness. The requirement for accuracy grounded in the documents makes retrieval the fitting choice.
- ✗
RAG because it does not require any labeled data
Why it's wrong here
RAG still requires a retrieval corpus and evaluation data to tune chunking and embeddings; the absence of labels is not what makes it correct here. It is tempting because RAG genuinely avoids supervised fine-tuning labels, and would be the right justification when no labelled examples exist but grounding is still required.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.