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Answer: Utilize Azure Active Directory (AAD) groups and Databricks workspace access control lists (ACLs).
1. **Azure Active Directory (AAD) groups**: These groups enable scalable user access management by categorizing users based on their roles or responsibilities. Assigning permissions through AAD groups ensures users have appropriate access to Databricks resources. 2. **Databricks workspace access control lists (ACLs)**: ACLs provide granular control over workspace resources, allowing you to specify read, write, or execute permissions for notebooks, tables, and clusters. 3. **Efficient access management**: Combining AAD groups with Databricks ACLs streamlines user permissions management, facilitating easy updates as roles evolve. 4. **Scalability and flexibility**: This approach supports organizational growth and role changes, ensuring users maintain correct access levels. Implementing RBAC with AAD groups and Databricks ACLs offers efficient, scalable, and flexible permission management with detailed control.
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What is the most effective method to implement Role-Based Access Control (RBAC) in Databricks for managing user access to notebooks, tables, and clusters?
A
Rely on network-level access controls to manage user access to Databricks resources.
B
Manually assign permissions to individual users within the Databricks workspace.
C
Utilize Azure Active Directory (AAD) groups and Databricks workspace access control lists (ACLs).
D
Implement an external IAM solution to manage roles and permissions across your Azure environment.
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