AWS Certified AI Practitioner AIF-C01 Practice Question

The approach describes transfer learning: the team begins with a foundation model that already learned general language representations, then fine-tunes (adapts) that model on a smaller, domain-specific dataset. This reuses most of the learned parameters, requires less data and compute than training from scratch, and speeds up development. Continuous pre-training keeps training on broad unlabeled data, prompt engineering changes only the inputs, and training from scratch builds a model with randomly initialized weights.