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?

⚠ Common exam trap

MLA-C01 often tests the distinction between data parallelism and model parallelism, and candidates may confuse SageMaker Distributed Data Parallelism with SageMaker Model Parallelism, or mistakenly think that Automatic Model Tuning or Debugger can distribute 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

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SageMaker Distributed Data Parallelism

SageMaker Distributed Data Parallelism is the correct choice because it is purpose-built to distribute training across multiple GPUs by replicating the model on each GPU and splitting the input data across them. It uses the AllReduce algorithm with custom communication optimizations (e.g., balanced fusion of gradients) to synchronize gradients efficiently, reducing communication overhead compared to standard Horovod or torch.distributed. This feature is integrated into the SageMaker training toolkit and works with TensorFlow, PyTorch, and MXNet, making it the right tool for data 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.

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SageMaker Distributed Data Parallelism
Why this is correct
SageMaker Distributed Data Parallelism splits each mini-batch across GPUs and uses AllReduce for gradient synchronisation, scaling TensorFlow training across multiple GPUs. It satisfies the stem's requirement for data parallelism specifically, unlike model parallelism, which partitions the model itself rather than replicating it per device.
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SageMaker Automatic Model Tuning
Why it's wrong here
Automatic Model Tuning searches hyperparameter combinations to optimise a chosen metric; it does not shard data or gradients across GPUs. It is tempting because it also manages distributed training jobs, but the requirement is data parallelism, which the SageMaker distributed data parallel library provides.
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SageMaker Debugger
Why it's wrong here
Debugger captures tensors and metrics during training to detect anomalies such as vanishing gradients; it does not partition data across devices. It is tempting because it hooks into distributed training jobs, but the requirement is data parallelism, which the SageMaker distributed data parallel library provides.
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SageMaker Model Parallelism
Why it's wrong here
Model Parallelism splits a single model's layers or tensors across devices, addressing models too large for one GPU's memory. It is tempting because it is a SageMaker distributed training library, but the stem requires data parallelism, which the distributed data parallel library provides instead.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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