AWS Certified AI Practitioner (AIF-C01) — Question 194
What is the benefit of fine-tuning a foundation model (FM)?
Answer options
- A. Fine-tuning reduces the FM's size and complexity and enables slower inference.
- B. Fine-tuning uses specific training data to retrain the FM from scratch to adapt to a specific use case.
- C. Fine-tuning keeps the FM's knowledge up to date by pre-training the FM on more recent data.
- D. Fine-tuning improves the performance of the FM on a specific task by further training the FM on new labeled data.
Correct answer: D
Explanation
The correct answer, D, is accurate because fine-tuning specifically aims to adapt a foundation model to perform better on a targeted task by training it with new labeled data. Option A is incorrect as fine-tuning typically aims to maintain or improve performance, not reduce size or increase complexity. Option B is wrong because fine-tuning does not involve retraining the FM from scratch, and option C is misleading since fine-tuning is about adapting existing knowledge rather than merely keeping it updated.