AIF-C01 Fundamentals of AI and ML Practice Question
A company is using Amazon SageMaker to train a large language model with hundreds of billions of parameters. The model does not fit into the memory of a single GPU. Which approach should they use to train the model efficiently?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between data parallelism and model parallelism, and the trap here is that candidates may confuse data parallelism (which splits data, not the model) as a solution for models that don't fit in memory, when in fact model parallelism is required for such cases.
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 SageMaker's model parallelism strategy with the SageMaker distributed training library
SageMaker's model parallelism strategy with the SageMaker distributed training library is specifically designed for training large models that do not fit into the memory of a single GPU. It partitions the model layers across multiple GPUs, enabling efficient training of models with hundreds of billions of parameters by overlapping computation and communication.
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 with more GPU memory, such as p4d.24xlarge
Why it's wrong here
Even with the largest instance, the model may still exceed GPU memory.
- ✗
Use SageMaker's data parallelism strategy
Why it's wrong here
Data parallelism replicates the model and splits data; model must fit in each GPU.
- ✓
Use SageMaker's model parallelism strategy with the SageMaker distributed training library
Why this is correct
Model parallelism splits the model across GPUs, enabling training of very large models.
- ✗
Reduce the model size by pruning layers until it fits into memory
Why it's wrong here
Reducing model size changes the architecture and may negatively impact accuracy.
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