A machine learning (ML) engineer uses Bayesian optimization for a hyperpara meter tuning job in Amazon SageMaker. The ML engineer uses precision as the objective metric. The ML engineer wants to use recall as the objective metric. The ML engineer also wants to expand the hyperparameter range for a new hyperparameter tuning job. The new hyperparameter range will include the range of the previously performed tuning job. Which approach will run the new hyperparameter tuning job in the LEAST amount of time?

Topic #: - All AWS Certified Machine Learning - Specialty Questions

A. Use a warm start hyperparameter tuning job.
B. Use a checkpointing hyperparameter tuning job.
C. Use the same random seed for the hyperparameter tuning job.
D. Use multiple jobs in parallel for the hyperparameter tuning job.

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