MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a neural network on a GPU instance in Amazon SageMaker. The training job fails with an 'OutOfMemoryError'. Which action should the data scientist take to resolve this issue?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that scaling up hardware (distributed training) solves memory errors, but the correct approach is to reduce per-instance memory load, typically by lowering batch size.
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
✓
Reduce the batch size in the training script.
An OutOfMemoryError during GPU training indicates that the GPU's memory is exhausted. Reducing the batch size directly decreases the memory footprint per training step, as fewer samples and their corresponding activations are stored simultaneously. This is the most immediate and effective fix without changing the instance type or training architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable automatic hyperparameter tuning.
Why it's wrong here
Hyperparameter tuning does not directly reduce memory usage.
- ✗
Switch to distributed training across multiple instances.
Why it's wrong here
Distributed training can increase memory usage per node.
- ✗
Use a smaller instance type with less GPU memory.
Why it's wrong here
Smaller instance has less memory, not more.
- ✓
Reduce the batch size in the training script.
Why this is correct
Smaller batch size reduces memory footprint.
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