Designing and Implementing a Data Science Solution on Azure — Question 155

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

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You have an Azure Machine Learning workspace.

You plan to tune model hyperparameters by using a sweep job.

You need to find a sampling method that supports early termination of low-performance jobs and continuous hyperparameters.

Solution: Use the random sampling method over the hyperparameter space.

Does the solution meet the goal?

Answer options

Correct answer: A

Explanation

The random sampling method is effective for hyperparameter tuning because it allows jobs with poor performance to be terminated early, which aligns with the goal of optimizing resource usage. The alternative option, 'No', would imply that the random sampling method does not support the required features, which is incorrect as it does allow for early termination and handles continuous hyperparameters well.