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A machine learning (ML) specialist is running an Amazon SageMaker hyperparameter optimization job for a...

A machine learning (ML) specialist is running an Amazon SageMaker hyperparameter optimization job for a model that is based on the XGBoost algorithm. The ML specialist selects Root Mean Square Error (RMSE) as the objective evaluation metric.

The ML specialist discovers that the model is overfitting and cannot generalize well on the validation data. The ML specialist decides to resolve the model overfitting by using SageMaker automatic model tuning (AMT).

Which solution will meet this requirement?

A.

Configure SageMaker AMT to use a static range of hyperparameter values.

B.

Configure SageMaker AMT to increase the number of parallel training jobs.

C.

Configure SageMaker AMT to stop training jobs early.

D.

Configure SageMaker AMT to run the training jobs with a warm start.

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