AIF-C01 Fundamentals of Generative AI Practice Question
A startup is fine-tuning a large language model (LLM) for code generation using Amazon SageMaker. They are using a p4d.24xlarge instance with a single GPU. The training process is extremely slow, taking over 48 hours for one epoch. The dataset is 10GB of code snippets. The company needs to iterate quickly. Which action would most significantly reduce training time without sacrificing model 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
✓
Enable distributed training using SageMaker’s data parallelism library across multiple GPUs
Distributed training across multiple GPUs and instances dramatically reduces time by parallelizing the workload. Increasing instance count or using a smaller model helps but may not be optimal. Spot instances could be unstable. Data parallelism is a standard technique for large models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable distributed training using SageMaker’s data parallelism library across multiple GPUs
Why this is correct
Distributed training scales across GPUs/nodes, significantly speeding up training while preserving model size.
- ✗
Switch to spot instances to reduce cost, not time
Why it's wrong here
Spot instances might be lower cost but do not inherently speed up training; they can even cause interruptions.
- ✗
Increase the batch size to use GPU memory more efficiently
Why it's wrong here
Larger batch size can improve throughput but may run out of memory; it's less impactful than distributed training.
- ✗
Use a smaller foundation model to reduce compute per step
Why it's wrong here
A smaller model may train faster but could reduce code generation quality; the goal is to maintain quality.
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