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Answer: Implement the upload_dashboard_dictionary function
The correct answer is C because the upload_dashboard_dictionary function from the Azure Machine Learning Fairness SDK is specifically designed to upload fairness assessment metrics and insights to Azure Machine Learning studio, where they can be visualized in the fairness dashboard. This function takes a dictionary containing fairness metrics and uploads it to the workspace. Option A (download_dashboard_by_upload_id) is for retrieving an existing dashboard, not uploading metrics. Option B (create_group_metric_set) is for creating the fairness metrics dataset but doesn't handle uploading to the studio. Option D (upload the training script) is unrelated to uploading fairness metrics. The community discussion shows 100% consensus on answer C, with multiple comments confirming this is the correct approach and providing code examples.
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You have a binary classification model registered in an Azure Machine Learning workspace. You used the Azure Machine Learning Fairness SDK to assess the model's fairness and developed a training script for the model on a local machine.
You need to upload the model fairness metrics to Azure Machine Learning studio.
What should you you do?
A
Implement the download_dashboard_by_upload_id function
B
Implement the create_group_metric_set function
C
Implement the upload_dashboard_dictionary function
D
Upload the training script
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