Designing and Implementing a Data Science Solution on Azure — Question 63

You are implementing hyperparameter tuning by using Bayesian sampling for a model training from a notebook. The notebook is in an Azure Machine Learning workspace that uses a compute cluster with 20 nodes.

The code implements Bandit termination policy with slack factor set to 0.2 and the HyperDriveConfig class instance with max_concurrent_runs set to 10.

You must increase effectiveness of the tuning process by improving sampling convergence.

You need to select which sampling convergence to use.

What should you select?

Answer options

• A. Set the value of slack factor of early_termination_policy to 09.
• B. Set the value of max_concurrent_runs of HyperDriveConfig to 4.
• C. Set the value of slack factor of early_termination_policy to 0.1.
• D. Set the value of max_concurrent_runs of HyperDriveConfig to 20.

Correct answer: B

Explanation

The correct answer is B because reducing the max_concurrent_runs allows for better resource allocation and can lead to improved convergence during the tuning process. The other options either do not effectively address the need for increased sampling convergence or may potentially hinder the tuning efficiency by either setting inappropriate slack factors or overloading the system with too many concurrent runs.