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A healthcare organization is developing a deep learning model on Amazon SageMaker to predict patient outcomes. The training dataset is substantial, and the organization aims to optimize the model's hyperparameters to minimize the validation dataset's loss function. Which hyperparameter tuning strategy should the organization use to achieve this goal with the least computational effort?