Designing and Implementing a Data Science Solution on Azure — Question 168

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 Bayesian sampling method over the hyperparameter space.

Does the solution meet the goal?

Answer options

Correct answer: B

Explanation

The proposed solution does not meet the goal because Bayesian sampling does not inherently support early termination of low-performance jobs. Instead, methods like Random sampling or Hyperband are more suited for this purpose as they can dynamically terminate runs based on performance metrics.