Práctica CompTIA SecAI+

A large language model (LLM) is fine-tuned on a company's internal support tickets so it can answer customer questions in the company's own voice. Compared to a foundation model used with retrieval-augmented generation (RAG) over the same tickets, what is a key security tradeoff of the fine-tuning approach?

• AFine-tuning makes it categorically impossible for the model to leak any training data
• BFine-tuning bakes the ticket content into the model's weights, making it harder to redact, audit, or remove specific records after the fact compared to RAG, where the source data stays in an external, editable store
• CRAG always costs more compute at inference time than fine-tuning, with no security implication
• DFine-tuning and RAG carry identical data-governance risk since both use the same underlying ticket data