MLS-C01 · Question #348
MLS-C01 Question #348: Real Exam Question with Answer & Explanation
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Question
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?
Options
- AUse a warm start hyperparameter tuning job.
- BUse a checkpointing hyperparameter tuning job.
- CUse the same random seed for the hyperparameter tuning job.
- DUse multiple jobs in parallel for the hyperparameter tuning job.
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