MLS-C01 Practice Question: Machine Learning Implementation and Operations
A team is training a large NLP model using SageMaker. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 61 GB GPU memory. Which action should the team take to resolve the issue without changing the model architecture?
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
Reducing the batch size in the training script decreases GPU memory usage per iteration, which can resolve the OutOfMemory error without changing the model architecture. Option A is incorrect because switching to a regression model changes the problem type. Option B is incorrect because increasing the number of epochs does not affect memory per step. Option C is incorrect because Managed Warm Pools are for reducing cold start times, not for memory issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a regression model
Why it's wrong here
Changing the model type does not directly address memory; it changes the task.
- ✗
Increase the number of epochs
Why it's wrong here
More epochs increase training time, not memory per step.
- ✗
Enable SageMaker Managed Warm Pools
Why it's wrong here
Warm pools reduce startup time, not memory usage.
- ✓
Reduce the batch size in the training script
Why this is correct
Smaller batch size reduces GPU memory consumption per step.
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