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MLS-C01 Modeling Practice Question

A team is using Amazon SageMaker to train a deep learning model. The training job is taking too long, and they want to reduce training time without significant accuracy loss. They have already tried increasing the number of instances. Which technique should they consider next?

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

Gradient accumulation

Gradient accumulation, is correct because it allows the use of larger effective batch sizes without increasing memory usage, which can lead to faster convergence and reduced training time. Option A, increasing L2 regularization, does not directly reduce training time; it helps prevent overfitting. Option B, reducing model complexity, can reduce training time but may cause significant accuracy loss due to underfitting. Option D, early stopping, can reduce training time by stopping training early but does not address the core issue of slow training per epoch; it may also halt training before convergence, risking accuracy loss.

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 L2 regularization

    Why it's wrong here

    Regularization does not reduce training time.

  • Reduce model complexity

    Why it's wrong here

    Reducing complexity may cause underfitting.

  • Gradient accumulation

    Why this is correct

    Gradient accumulation simulates larger batch sizes, improving convergence speed.

  • Early stopping

    Why it's wrong here

    Early stopping stops training early but does not reduce per-epoch time.

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