MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is training a deep learning model on Amazon SageMaker. The training job is failing with an out-of-memory error. Which SageMaker feature should the company use to resolve this issue without changing the instance type?
⚠ Common exam trap
Many candidates confuse diagnostic tools (Debugger, Profiler) with solutions, or mistakenly think cost-saving features (Savings Plans, Spot Training) can fix memory errors, when the correct answer requires understanding that model parallelism directly addresses GPU memory limits by distributing the model.
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 SageMaker distributed training with model parallelism
SageMaker's distributed training with model parallelism splits the model's layers across multiple GPUs, reducing the memory footprint per GPU. This allows the company to train a large model that exceeds a single GPU's memory without changing the instance type. Model parallelism is specifically designed to handle out-of-memory errors by distributing the model parameters, gradients, and optimizer states across devices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use SageMaker distributed training with model parallelism
Why this is correct
Model parallelism splits the model across multiple instances, reducing memory per instance.
- ✗
Use SageMaker Savings Plans
Why it's wrong here
Savings Plans are for cost savings, not memory.
- ✗
Enable SageMaker Managed Spot Training
Why it's wrong here
Spot Training reduces cost but does not affect memory.
- ✗
Use SageMaker Debugger to monitor memory usage
Why it's wrong here
Debugger monitors but does not reduce memory usage.
- ✗
Enable SageMaker Profiler to profile memory
Why it's wrong here
Profiler analyzes but does not fix the issue.
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