You work for a transportation company as a machine learning specialist. You are currently working on a project to optimize container truck routes to minimize empty container travel. For example, as a truck delivers its payload to a destination, you want to have the container loaded for another route. You don’t want the truck to move to another destination with an empty container. You have selected the SageMaker XGBoost algorithm for your model. You now need to tune your hyperparameters to get the optimum performance out of your model. You have chosen the Area Under the Curve (AUC) metric as your objective metric for your hyperparameter tuning job.
Which algorithm should you use as the SageMaker hyperparameter tuning algorithm to get your results in the minimal number of training jobs?

Answer options:

A.Random search
B.Bayesian Search
C.Linear Search
D.Depth First Search

Answer: B
Option A is incorrect. SageMaker uses two types of models to search for the optimum hyperparameters for your model: Random Search and Bayesian Search. For most models, Bayesian Search requires less training jobs to reach your optimal hyperparameter settings. (See the Amazon Machine Learning blog titled Amazon SageMaker automatic model tuning now supports random search and hyperparameter scaling)
Option B is correct. SageMaker uses two types of models to search for the optimum hyperparameters for your model: Random Search and Bayesian Search. For most models, Bayesian Search requires less training jobs to reach your optimal hyperparameter settings. (See the Amazon Machine Learning blog titled Amazon SageMaker automatic model tuning now supports random search and hyperparameter scaling)
Option C is incorrect.SageMaker hyperparameter tuning does not use Linear Search as a hyperparameter tuning model.
Option D is incorrect. SageMaker hyperparameter tuning does not use Depth First Search as a hyperparameter tuning model.
Reference:
Please see the Amazon SageMaker developer guide titled Configure and Launch a Hyperparameter Tuning Job, the Amazon SageMaker developer guide titled Automatic Model Tuning, and the Amazon SageMaker developer guide titled How Hyperparameter Tuning Works.