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Answer: Use Databricks Workspace ACLs (Access Control Lists) to restrict access to specific users.
The most secure and controlled method for sharing a Databricks notebook with external collaborators, especially when sensitive data is involved, is to use Databricks Workspace ACLs (Access Control Lists) to restrict access to specific users. This approach offers granular access control, allowing precise permissions to be defined for each user or group, ensuring collaborators only interact with necessary components. It maintains a controlled environment within the Databricks platform, avoiding reliance on insecure external tools or file transfers. Additionally, ACLs enable auditing and monitoring of user activity, enhancing data governance and accountability. Other options, such as exporting and emailing the notebook or using a shared secret key, pose significant security risks by potentially exposing sensitive data outside the controlled environment. Utilizing Databricks Managed Identity is more suited for external services rather than individual collaborators and may not provide the necessary granular access control for sensitive data.
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In a Databricks environment, a data scientist is handling a machine learning project that includes sensitive data. This project necessitates collaboration with external stakeholders who should have restricted access to the data. What is the most secure method for the data scientist to share the Databricks notebook with these external collaborators?
A
Create a shared secret key for external collaborators to access the notebook.
B
Export the notebook and share it via email with external collaborators.
C
Utilize Databricks Managed Identity to grant temporary access to external collaborators.
D
Use Databricks Workspace ACLs (Access Control Lists) to restrict access to specific users.