AWS Certified Machine Learning – Specialty — Question 317
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?
Answer options
- 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.
Correct answer: A
Explanation
A warm start hyperparameter tuning job in Amazon SageMaker allows you to leverage the results of previous tuning jobs as a baseline, which substantially reduces the search time even when modifying the objective metric or expanding the hyperparameter ranges. Checkpointing is designed to resume individual model training runs rather than accelerating the search space optimization. Parallel execution and fixed random seeds do not utilize historical search data to optimize the tuning process efficiently.