Explanation
The answer is, Use dynamic view functions.
Here is an example that limits access to rows based on the user being part managers group, in the below view if a user is not a part of the manager's group you can only see rows where the total amount is <= 1000000 Dynamic view function to filter rows
1.CREATE VIEW sales_redacted AS
2.SELECT user_id, country, product, total
3.FROM sales_raw
4.WHERE CASE WHEN is_member('managers') THEN TRUE ELSE total <= 1000000 END; Dynamic view function to hide a column data based on user's access,
1.CREATE VIEW sales_redacted AS
2.SELECT user_id,
3. CASE WHEN is_member('auditors') THEN email ELSE 'REDACTED' END AS email,
4. country,
5. product,
6. total
7.FROM sales_raw
Please review below for more details
https://docs.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#dynamic-v

Explanation
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.

Explanation
The answer is, Account Level
The classic access control list (tables, workspace, cluster) is at the workspace level, Unity catalog is at the account level and can manage all the workspaces in an Account.

Explanation
The answer is global temporary views can be still accessed even if the notebook is detached and attached There are two types of temporary views that can be created Local and Global
* A local temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
* A global temporary view is available to all the notebooks in the cluster, even if the notebook is detached and reattached it can still be accessible but if a cluster is restarted the global temporary view is lost.