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Machine Learning Implementation and OperationseasyMultiple ChoiceObjective-mapped

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.

Visual reference

Client Recursive Resolver Root DNS (13 root servers) TLD DNS (.com, .org, …) Authoritative example.com query IP addr answer

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