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MLS-C01 Modeling Practice Question

A data scientist is training a model using Amazon SageMaker. The training job is running on GPU instances, but the GPU utilization is low. Which TWO actions could improve GPU utilization?

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 a larger instance with multiple GPUs

Using a larger instance with multiple GPUs allows more parallel processing, improving GPU utilization. Option C is correct because increasing the batch size provides more data per step, better utilizing GPU parallelism. Option A is incorrect because increasing epochs does not affect utilization per step. Option D is incorrect because switching to CPU instances would not utilize GPU. Option E is incorrect because decreasing batch size reduces parallelism and lowers GPU utilization.

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 number of epochs

    Why it's wrong here

    Increasing the number of epochs does not affect GPU utilization per step; it only increases training time.

  • Use a larger instance with multiple GPUs

    Why this is correct

    Using a larger instance with multiple GPUs provides more parallel compute resources, improving overall GPU utilization.

  • Increase the batch size

    Why this is correct

    Increasing the batch size allows more data to be processed in parallel on the GPU, increasing utilization.

  • Switch to CPU instances

    Why it's wrong here

    Switching to CPU instances would not utilize GPU resources, reducing utilization to zero.

  • Decrease the batch size

    Why it's wrong here

    Decreasing the batch size reduces the amount of parallel computation, lowering GPU utilization.

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Last reviewed: Jun 20, 2026

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