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?
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 retrieves exact passages and grounds answers in the source, which is critical for accuracy and citation in scientific domains. Fine-tuning may memorize but can hallucinate.
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 adapts the model but does not guarantee grounding; the model may still generate plausible but incorrect information not in the corpus.
- ✗
Fine-tuning because it is faster at inference time
Why it's wrong here
Fine-tuning can be faster at inference than RAG (which requires retrieval), but accuracy and grounding are more important for scientific QA.
- ✓
RAG because it retrieves and grounds answers in the source documents
Why this is correct
RAG retrieves relevant passages and passes them as context, ensuring the answer is directly supported by the source, reducing hallucination.
- ✗
RAG because it does not require any labeled data
Why it's wrong here
RAG still requires some labeled data for evaluation and possibly for retrieval tuning; but the primary advantage is grounding.
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.