MLA-C01 ML Model Development Practice Question
A machine learning engineer is training a TensorFlow model using SageMaker with distributed training. They need to implement data parallelism across multiple GPUs. Which SageMaker feature should they use to distribute the training?
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 Distributed Data Parallelism
SageMaker's distributed data parallelism library handles splitting data across GPUs and synchronizing gradients, optimized for TensorFlow and PyTorch.
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 Parallelism
Why this is correct
This library implements data parallelism for SageMaker training.
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SageMaker Automatic Model Tuning
Why it's wrong here
Tuning is for hyperparameter optimization, not distributing training.
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SageMaker Debugger
Why it's wrong here
Debugger monitors training, not distributes it.
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SageMaker Model Parallelism
Why it's wrong here
Model parallelism splits model layers across devices, not data.
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