{"id":577,"date":"2023-01-14T11:10:23","date_gmt":"2023-01-14T11:10:23","guid":{"rendered":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/aws-certified-machine-learning-specialty-mls-c01-question178\/"},"modified":"2023-01-14T11:10:23","modified_gmt":"2023-01-14T11:10:23","slug":"aws-certified-machine-learning-specialty-mls-c01-question178","status":"publish","type":"post","link":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/aws-certified-machine-learning-specialty-mls-c01-question178\/","title":{"rendered":"AWS Certified Machine Learning – Specialty MLS-C01 – Question178"},"content":{"rendered":"
A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:
\n
\nDuring the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
\nWhich solution provides the MOST accurate result?

A.<\/strong> Modify the HPO configuration as follows:
\n
\nSelect the most accurate hyperparameter configuration form this HPO job.
B.<\/strong> Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:
\n[0.01, 0.1]
\n[0.001, 0.01]
\n[0.0001, 0.001]
\nSelect the most accurate hyperparameter configuration form these three HPO jobs.
C.<\/strong> Modify the HPO configuration as follows:
\n
\nSelect the most accurate hyperparameter configuration form this training job.
D.<\/strong> Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:
\n[0.01, 0.1]
\n[0.001, 0.01]
\n[0.0001, 0.001]
\nSelect the most accurate hyperparameter configuration form these three HPO jobs.<\/div>\n

<\/p>\n