MLA-C01 ML Model Development Practice Question
A team is training a large language model using PyTorch on SageMaker. They need to reduce training time. The model has 10 billion parameters. 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 with SageMaker distributed
For large models that do not fit into GPU memory, model parallelism is required. Data parallelism replicates the model on each GPU, which would cause out-of-memory errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data parallelism with Horovod
Why it's wrong here
Data parallelism replicates the entire model, which would exceed GPU memory for a 10B parameter model.
- ✗
Single GPU training
Why it's wrong here
Single GPU is too slow and likely cannot fit the model.
- ✗
Use a larger instance type without parallelism
Why it's wrong here
Even the largest GPU instances may not fit a 10B model, and parallelism is still beneficial for speed.
- ✓
Model parallelism with SageMaker distributed
Why this is correct
Model parallelism partitions the model across GPUs, enabling training of large models.
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Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team is training a large language model and needs to split the model layers across multiple GPUs due to memory constraints. Which distributed training strategy should they use?
medium- A.Data parallelism
- B.Hyperparameter tuning
- C.Autopilot
- ✓ D.Model parallelism
Variation 2. A team is training a large language model on SageMaker using PyTorch with data parallelism. The model is too large to fit on a single GPU. Which distributed training strategy should they use to split the model across multiple GPUs?
medium- ✓ A.Model parallelism
- B.Tensor parallelism
- C.Data parallelism
- D.Pipeline parallelism
Why A: Model parallelism splits the model itself across devices, which is necessary when the model is too large for one GPU. SageMaker's model parallelism library supports this.
JA
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