- A
Use a larger instance type with more vCPUs
Why wrong: Larger instances increase cost without addressing the core inefficiency of full fine-tuning.
- B
Increase the batch size to the maximum possible
Why wrong: Large batch sizes may exceed GPU memory and cause training failures.
- C
Use the full model weights and train on a single GPU
Why wrong: Training full weights on a single GPU is slow and may not converge for large models.
- D
Use Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA
PEFT reduces memory and time by updating only a small number of parameters.
Question 265 of 1,000
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A machine learning team is fine-tuning a foundation model using Amazon SageMaker. They need to optimize training time and cost. Which approach should they take?
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) techniques like LoRA
Option D is correct because Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) significantly reduce the number of trainable parameters by injecting low-rank matrices into the model layers, while keeping the original weights frozen. This drastically lowers memory usage and computational cost, enabling faster training and reduced GPU hours on SageMaker without sacrificing model quality.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 a larger instance type with more vCPUs
Why it's wrong here
Larger instances increase cost without addressing the core inefficiency of full fine-tuning.
- ✗
Increase the batch size to the maximum possible
Why it's wrong here
Large batch sizes may exceed GPU memory and cause training failures.
- ✗
Use the full model weights and train on a single GPU
Why it's wrong here
Training full weights on a single GPU is slow and may not converge for large models.
- ✓
Use Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA
Why this is correct
PEFT reduces memory and time by updating only a small number of parameters.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common mistake is thinking that larger instances always improve performance, but Amazon SageMaker optimization often relies on algorithmic efficiency like PEFT rather than just scaling hardware.
Detailed technical explanation
How to think about this question
LoRA works by decomposing weight updates into low-rank matrices (typically rank r=8 or 16), which are trained while the original weights remain frozen; this reduces the number of trainable parameters by orders of magnitude (e.g., from billions to millions). In SageMaker, this allows fine-tuning on a single GPU instance (like ml.g5.2xlarge) instead of multi-GPU clusters, directly lowering cost. A subtle behavior is that LoRA adapters can be swapped at inference time without reloading the base model, enabling multi-task serving from a single deployment.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA — Option D is correct because Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) significantly reduce the number of trainable parameters by injecting low-rank matrices into the model layers, while keeping the original weights frozen. This drastically lowers memory usage and computational cost, enabling faster training and reduced GPU hours on SageMaker without sacrificing model quality.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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