Which THREE steps are typically involved in fine-tuning a foundation model? (Select THREE.)
Fine-tuning adapts a foundation model's weights to a target task, which requires supervised examples the model can learn from. Preparing a labelled dataset specific to the target domain supplies those input-output pairs, satisfying the scenario's need for task-relevant training data rather than relying on the model's general pre-trained knowledge.
Why this answer
Fine-tuning a foundation model begins with selecting an appropriate pre-trained foundation model as the starting point (D), since the whole point of fine-tuning is to adapt existing general-purpose weights rather than train from scratch. Next, you must prepare a labeled dataset specific to the target domain (B), because supervised fine-tuning requires task-relevant input-output pairs to steer the model toward the desired behavior. Then you train the model on that domain dataset with a lower learning rate (C), which is standard practice to avoid catastrophic forgetting and to gently nudge the pre-trained weights instead of overwriting them.
Option A is incorrect because deploying the model immediately without additional training is the opposite of fine-tuning—it describes using the base model as-is. Option E is incorrect because fine-tuning does not require choosing an architecture with more parameters than the base model; you typically fine-tune the same pre-trained architecture, and increasing parameter count is not a fine-tuning step.
Exam trap
AWS often tests the distinction between fine-tuning and other adaptation methods (like prompt engineering or retrieval-augmented generation), and the trap here is that candidates might think fine-tuning requires a larger model or no additional data, when in fact it requires a labeled dataset and the same architecture.