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MLA-C01 ML Model Development Practice Question

A data scientist is training an XGBoost model on a large tabular dataset using SageMaker. The training job is taking too long. The scientist wants to reduce training time while maintaining model quality. Which action should the scientist take?

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 data parallelism across multiple instances

Using SageMaker's managed spot training can significantly reduce cost, but it may cause interruptions. The best approach to reduce training time is to use distributed data parallelism with multiple instances. Increasing instance type can also speed up training, but distributed training is more scalable. Using Hyperband is for hyperparameter tuning, not for reducing training time directly. Converting to a different algorithm is not necessary.

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 data parallelism across multiple instances

    Why this is correct

    Distributed data parallelism speeds up training by splitting data across multiple instances.

  • Enable SageMaker managed spot training

    Why it's wrong here

    Spot training reduces cost, not necessarily training time, and may cause interruptions.

  • Switch to Hyperband for hyperparameter tuning

    Why it's wrong here

    Hyperband is for tuning, not for reducing training time of a given job.

  • Convert the XGBoost model to a Linear Learner model

    Why it's wrong here

    Changing the algorithm may not maintain model quality and is not a direct solution to reduce training time.

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