MLA-C01 ML Model Development Practice Question
A company is training a large computer vision model using SageMaker. The training dataset is 500 GB and the model has 1 billion parameters. The team needs to minimize training time. Which distributed training strategy 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
✓
Model parallelism
Model parallelism splits the model layers across multiple GPUs, which is necessary when the model is too large to fit on a single GPU. Data parallelism replicates the model on each GPU and splits the data, but is limited by the memory of a single GPU.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pipeline parallelism
Why it's wrong here
Pipeline parallelism is a form of model parallelism but not the primary term used in SageMaker; SageMaker’s model parallelism handles layer partitioning.
- ✗
Sharded data parallelism
Why it's wrong here
Sharded data parallelism (like ZeRO) reduces memory but still requires the model to fit across GPUs; for a 1B parameter model, pure data parallelism may be insufficient.
- ✓
Model parallelism
Why this is correct
Model parallelism partitions the model layers across GPUs, enabling training of large models that don't fit on one GPU.
- ✗
Data parallelism
Why it's wrong here
Data parallelism replicates the entire model on each GPU; a 1B parameter model likely exceeds GPU memory.
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