MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist wants to speed up training by using distributed training across multiple GPUs. Which SageMaker feature or configuration should the data scientist use?
⚠ Common exam trap
Many exam-takers confuse model parallelism (splitting the model) with data parallelism (splitting the data), and incorrectly choose model parallelism when the scenario clearly describes a training speed issue solvable by distributing data across GPUs.
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 Data Parallelism library
The SageMaker Data Parallelism library is specifically designed to distribute training across multiple GPUs by splitting the input data across workers, which reduces per-GPU computation time and accelerates training for deep learning models. This library uses optimized all-reduce algorithms (e.g., Ring AllReduce) to synchronize gradients efficiently, making it ideal for speeding up image classification tasks that are data-intensive.
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 Debugger
Why it's wrong here
SageMaker Debugger monitors training jobs but does not accelerate training.
- ✗
Model parallelism in SageMaker
Why it's wrong here
Model parallelism splits the model across devices, but it is typically used for very large models that do not fit in memory, not for speeding up training of standard image classifiers.
- ✗
SageMaker hyperparameter tuning
Why it's wrong here
Hyperparameter tuning runs multiple training jobs with different hyperparameters but does not speed up a single training job.
- ✓
SageMaker Data Parallelism library
Why this is correct
The SageMaker Data Parallelism library distributes data across multiple GPUs, reducing training time for large datasets.
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