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MLA-C01 Practice Question: A data scientist is training a deep learning…
A data scientist is training a deep learning model using SageMaker and wants to use distributed training across multiple GPUs to reduce training time. Which TWO actions should the scientist take to configure distributed training? (Select TWO.)
⚠ Common exam trap
Many exam-takers confuse single-instance multi-GPU training (option E) with true distributed training across multiple instances, or assume manual data sharding (option C) is required when SageMaker automates it.
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
✓
Use the SageMaker distributed data parallelism library
The SageMaker distributed data parallelism library (option B) automatically partitions training data and synchronizes gradients across multiple GPUs, reducing training time without manual data splitting. Configuring the SageMaker estimator with a distribution parameter (option D) enables this library by specifying the distribution strategy (e.g., 'torch_distributed' or 'tensorflow_distributed'), which is required to activate distributed training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of epochs to match the number of GPUs
Why it's wrong here
Epochs are not related to distributed training; reducing epochs underfits.
- ✓
Use the SageMaker distributed data parallelism library
Why this is correct
The library automatically distributes data across GPUs.
- ✗
Manually split the training data into shards and upload to S3
Why it's wrong here
Manual sharding is unnecessary; SageMaker handles data distribution.
- ✓
Configure the SageMaker estimator with a distribution parameter
Why this is correct
The distribution parameter specifies the strategy (e.g., 'data_parallel').
- ✗
Set the instance count to 1 with a multi-GPU instance
Why it's wrong here
Single instance, even with multiple GPUs, is not distributed training across instances.
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