AIF-C01 Fundamentals of Generative AI Practice Question
Which THREE steps are typically involved in fine-tuning a foundation model? (Select THREE.)
⚠ Common 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.
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
✓
Prepare a labeled dataset specific to the target domain
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model immediately without additional training
Why it's wrong here
Fine-tuning requires further training on task-specific data; deploying straight away skips that step entirely. Immediate deployment suits a pre-trained model used as-is via prompting or an API, where no weight updates are needed, but it cannot satisfy a scenario demanding adaptation to a specialised dataset.
- ✓
Prepare a labeled dataset specific to the target domain
Why this is correct
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.
- ✓
Train the model on the domain dataset with a lower learning rate
Why this is correct
Fine-tuning adapts a pre-trained foundation model by continuing training on the domain dataset, and a lower learning rate preserves the acquired weights while nudging them toward the target domain. This satisfies the stem's requirement for a typical fine-tuning step, since the reduced rate prevents catastrophic forgetting of the base model's general capabilities.
- ✓
Select a pre-trained foundation model as the starting point
Why this is correct
Selecting a pre-trained foundation model provides the learned weights and architecture that fine-tuning adapts, rather than training from scratch. This satisfies the scenario's requirement for a starting point, since fine-tuning adjusts an existing model's parameters on task-specific data. Without this base, the process would be full pre-training, not fine-tuning.
- ✗
Choose a model architecture with more parameters than the base model
Why it's wrong here
Fine-tuning adapts an existing pre-trained model's weights; it never swaps in a larger architecture, since parameter count is fixed at selection time. Enlarging the architecture is a pre-training design decision, so it addresses no fine-tuning step such as dataset preparation, hyperparameter configuration or evaluation.
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