AIF-C01 Fundamentals of Generative AI Practice Question
A research team is using Amazon SageMaker to fine-tune a large language model. They want to optimize training cost and time without sacrificing model quality. Which THREE strategies should they implement? (Choose 3)
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that simply scaling up hardware (larger instances) or maximizing batch size is the best optimization strategy, when in fact algorithmic efficiency (PEFT, mixed precision) and cost-saving infrastructure (spot instances) are the correct approaches for balancing cost, time, and quality.
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
✓
Apply parameter-efficient fine-tuning (PEFT) techniques like LoRA.
Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) freeze the pre-trained model weights and inject trainable rank decomposition matrices into specific layers. This drastically reduces the number of trainable parameters (often by 10,000x), lowering memory and compute requirements while preserving model quality, making it ideal for cost- and time-sensitive 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 a larger instance type with more GPUs.
Why it's wrong here
Larger instances increase cost, not necessarily cost efficiency.
- ✓
Apply parameter-efficient fine-tuning (PEFT) techniques like LoRA.
Why this is correct
LoRA fine-tunes a small subset of parameters, reducing compute and memory.
- ✗
Increase the batch size to the maximum that fits in GPU memory.
Why it's wrong here
Larger batch size can affect convergence and doesn't always improve quality.
- ✓
Use managed spot training with checkpointing.
Why this is correct
Spot instances are cheaper and SageMaker handles interruptions via checkpoints.
- ✓
Enable mixed precision training (FP16).
Why this is correct
FP16 reduces memory usage and speeds up training with minimal quality loss.
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