You work for a software company that produces online sports betting app. You are on the machine learning team responsible for building a model that predicts the likelihood of registered users to wager on a given event based on several features of sports events offered in the app. You and your team have selected the Linear Learner algorithm and have trained your model. You now wish to find the best set of hyperparameters for your model. You have chosen to use SageMaker’s automatic model tuning, and you have set your objective to validation:precision in your hyperparameter tuning job.
How do you pass your tuning job settings into your hyperparameter tuning job? (Select THREE)

Answer options:

A.Define a JSON object and pass it as the value of the HyperParameterConfig to the HyperParameterTuningJob.
B.Define a JSON object and pass it as the value of the HyperParameterTuningJobConfig to the CreateHyperParameterTuningJob.
C.In the JSON object specify the ranges of the hyperparameters you want to tune.
D.In the JSON object specify the limits of the hyperparameters you want to tune.
E.In the JSON object specify the objective metric for the hyperparameter tuning job.
F.In the JSON object specify the MaxSequentialTrainingJobs parameter in the ResourceLimits section.

Answers: B, C, E
Option A is incorrect. The correct name of the value you use to pass your JSON object is HyperParameterTuningJobConfig, and the name of the job is CreateHyperParameterTuningJob.
Option B is correct. To specify the hyperparameter settings for your hyperparameter tuning job, you pass a JSON object as the HyperParameterTuningJobConfig parameter to the job named CreateHyperParameterTuningJob.
Option C is correct.You specify the ranges of the hyperparameters you want to tune in the ParameterRanges section of the HyperParameterTuningJobConfig.
Option D is incorrect. You specify the ranges of the hyperparameters you want to tune in the ParameterRanges section of the HyperParameterTuningJobConfig, not the limits of the hyperparameters.
Option E is correct. In the HyperParameterTuningJobObjective section of the HyperParameterTuningJobConfig, you set MetricName to the objective metric for the hyperparameter tuning job.
Option F is incorrect. There is no MaxSequentialTrainingJobs parameter in the ResourceLimits section of the HyperParameterTuningJobConfig.
Reference:
Please see the Amazon SageMaker developer guide titled Automatic Model Tuning, and the Amazon SageMaker developer guide titled Configure and Launch a Hyperparameter Tuning Job.