AIF-C01 Applications of Foundation Models Practice Question
A team is fine-tuning a foundation model using SageMaker. They want to minimize training time while keeping the model's original knowledge. Which technique is BEST suited?
⚠ Common exam trap
AWS often tests the distinction between techniques that modify the model (fine-tuning) versus those that only change the input (prompt engineering), and the trap here is that candidates may choose distributed training (Option B) thinking it reduces time, but it does not address parameter efficiency or knowledge preservation as directly as PEFT.
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
✓
Use Parameter Efficient Fine-Tuning (PEFT) such as LoRA
Parameter Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are best suited because they freeze the pre-trained model weights and inject trainable low-rank matrices into specific layers, drastically reducing the number of trainable parameters. This minimizes training time and computational cost while preserving the model's original knowledge, as only a small fraction of parameters are updated during fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Parameter Efficient Fine-Tuning (PEFT) such as LoRA
Why this is correct
PEFT with LoRA freezes the base weights and trains small low-rank adapter matrices, cutting trainable parameters and GPU time substantially. Because original weights remain unchanged, the model's pretrained knowledge is preserved, satisfying both the speed and retention constraints.
- ✗
Use distributed training across multiple GPUs
Why it's wrong here
Distributed training across multiple GPUs shortens wall-clock training time but splits the workload without preventing catastrophic forgetting of the model's original knowledge. It is the right technique when training duration alone is the constraint, not when preserving prior knowledge matters.
- ✗
Use prompt engineering instead of fine-tuning
Why it's wrong here
Prompt engineering leaves model weights untouched, so it cannot adapt the model to the new dataset's task, which is the stated goal of fine-tuning. It is tempting because it avoids training entirely, and it would be correct when the base model already handles the task and only output steering is needed.
- ✗
Full fine-tuning on the new dataset
Why it's wrong here
Full fine-tuning updates every parameter, which maximises training time and risks catastrophic forgetting of the model's original knowledge — the two things the team wants to avoid. It is tempting because it can yield the highest task accuracy, and it would be correct when ample compute is available and preserving prior knowledge is irrelevant.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.