MLS-C01 Modeling Practice Question
A research lab is training a large language model (LLM) on SageMaker using PyTorch. The model has 1 billion parameters and does not fit on a single GPU. They have access to a cluster of 16 p4d.24xlarge instances (each with 8 A100 GPUs). They need to train the model with minimal changes to the training script. Which SageMaker feature should they use?
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
✓
SageMaker's model parallelism with automatic partitioning
SageMaker's model parallelism is designed for large models that don't fit on a single device.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker's model parallelism with automatic partitioning
Why this is correct
Model parallelism splits the model across GPUs, and SageMaker's library automates this.
- ✗
SageMaker's distributed data parallelism with Horovod
Why it's wrong here
Data parallelism requires the model to fit on one GPU; LLMs often need model parallelism.
- ✗
Use SageMaker's built-in BlazingText algorithm
Why it's wrong here
BlazingText is for word embeddings, not LLMs.
- ✗
SageMaker's managed spot training with checkpointing
Why it's wrong here
Spot training addresses cost, not model size.
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