MLA-C01 · Question #226
An ML engineer decides to use Amazon SageMaker AI automated model tuning (AMT) for hyperparameter optimization (HPO). The ML engineer requires a tuning strategy that uses regression to slowly and…
The correct answer is C. Bayesian optimization. Bayesian optimization uses a surrogate regression model built from previous tuning jobs to sequentially choose the next hyperparameter values, balancing exploration and exploitation. This approach is designed to work efficiently even when hyperparameter ranges are small.
Question
An ML engineer decides to use Amazon SageMaker AI automated model tuning (AMT) for hyperparameter optimization (HPO). The ML engineer requires a tuning strategy that uses regression to slowly and sequentially select the next set of hyperparameters based on previous runs. The strategy must work across small hyperparameter ranges. Which solution will meet these requirements?
Options
- AGrid search
- BRandom search
- CBayesian optimization
- DHyperband
How the community answered
(50 responses)- A4% (2)
- B2% (1)
- C88% (44)
- D6% (3)
Explanation
Bayesian optimization uses a surrogate regression model built from previous tuning jobs to sequentially choose the next hyperparameter values, balancing exploration and exploitation. This approach is designed to work efficiently even when hyperparameter ranges are small.
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