MLA-C01 ML Model Development Practice Question
A team is training a large language model using SageMaker with multiple GPUs. They need to reduce training time by splitting the model across devices due to memory constraints. 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
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 Distributed Data Parallel (SMDDP)
Why it's wrong here
SMDDP is an optimized data parallelism library.
- ✗
Data parallelism
Why it's wrong here
Data parallelism replicates the model on each device; it does not reduce memory per device.
- ✗
SageMaker Distributed Model Parallel (SMDMP)
Why it's wrong here
SMDMP is correct but the option name is incomplete; however, model parallelism is the general strategy.
- ✓
Model parallelism
Why this is correct
Model parallelism splits the model across devices, reducing memory per device.
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