Question 1

Which parity constraint can be used with any of the mitigation algorithms to minimize disparity in the selection rate across sensitive feature groups?

Question 2

Which parity constraint can be used with any of the mitigation algorithms to minimize disparity in combined true positive rate and false_positive_rate across sensitive feature groups?

Question 3

You are training a binary classification model to determine who should be targeted in a marketing campaign.

How can you assess if the model is fair and will not discriminate based on ethnicity?

Question 4

When deploying a new real-time service, Application Insights can be enabled in the deployment configuration for the service.

How would you code that using the SDK?

Question 5

For web services that have already been deployed, you can update them and enable the Application Insight using the Azure ML SDK.

How would you code that?

Question 6

You want to back fill a dataset monitor based on monthly changes in data for the previous 3 months. How would you code that in the SDK?

Question 7

You want to schedule a data drift monitor to run every day and send an alert if the drift magnitude is greater than 0.2. How would you code that in Python?

alert_email = AlertConfiguration('data_scientists@contoso.com') monitor = DataDriftDetector.create_from_datasets(ws, 'dataset-drift-detector', baseline_data_set, target_data_set, compute_target=cpu_cluster, frequency='Day', latency=2, drift_threshold=.4)