A security engineer is reviewing the access control configuration for an Azure Machine Learning workspace. Multiple data scientists need to run experiments and deploy models but should not be able to modify workspace-level settings or manage other users' permissions. Which built-in role should be assigned?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because the 'AzureML Data Scientist' built-in role is specifically designed for data scientists who need to run experiments, train models, and deploy to compute targets within an Azure Machine Learning workspace without having permission to modify workspace-level settings or manage RBAC assignments. A is incorrect because Contributor grants broad permissions including modifying workspace settings, which exceeds the required scope.
Full explanation below image
Full Explanation
B is correct because the 'AzureML Data Scientist' built-in role is specifically designed for data scientists who need to run experiments, train models, and deploy to compute targets within an Azure Machine Learning workspace without having permission to modify workspace-level settings or manage RBAC assignments. A is incorrect because Contributor grants broad permissions including modifying workspace settings, which exceeds the required scope. C is incorrect because AzureML Compute Operator grants permissions related to compute resource management, not full data science workflow permissions. D is incorrect because Cognitive Services Contributor grants broad access to all Cognitive Services resources, not scoped Azure ML workspace permissions.