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MLS-C01 Modeling Practice Question

A team is using SageMaker to train a deep learning model for image classification. The training job is failing with a 'CUDA out of memory' error. The team is using a p3.2xlarge instance (1 GPU, 16 GB GPU memory). The dataset consists of 256x256 RGB images. Which action is MOST likely to resolve the error without changing the instance type?

⚠ Common exam trap

A common mix-up: candidates confuse 'CUDA out of memory' with a performance issue and incorrectly choose to increase batch size for efficiency, when in fact the error is a hard memory limit that requires reducing memory usage.

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

The 'CUDA out of memory' error indicates that the GPU memory is exhausted. Reducing the batch size directly decreases the memory footprint per training step, allowing the model to fit within the 16 GB GPU memory of the p3.2xlarge instance. This is the most direct and effective fix without changing the instance type.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Increase the batch size to utilize GPU more efficiently

    Why it's wrong here

    Larger batch size increases memory usage, potentially causing OOM errors.

  • Enable automatic model tuning to optimize hyperparameters

    Why it's wrong here

    Automatic model tuning does not directly address memory errors.

  • Use Spot Instances to reduce cost

    Why it's wrong here

    Spot Instances do not affect GPU memory availability.

  • Reduce the batch size

    Why this is correct

    Smaller batch size reduces memory footprint per iteration, resolving OOM errors.

Visual reference

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