Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.
Databricks documentation on access control and permissions for notebooks within the workspace (https://docs.databricks.com/security/access-control/workspace-acl.html).

Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:
* Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html
* DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table

Explanation
The answer is User requires USAGE privilege on Sales schema,
Data object privileges - Azure Databricks | Microsoft Docs
GRANT USAGE ON SCHEMA sales TO [email protected];
*USAGE: does not give any abilities, but is an additional requirement to perform any action on a schema object.

Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Unity Catalog supports dynamic column masking, where masking logic can be implemented using SQL functions or UDFs that reference external mapping tables or metadata for context-aware access control.
By referencing the group_access table inside the masking function, the mask dynamically evaluates whether a requesting user belongs to an authorized group. If permitted, the original column value is returned; otherwise, a masked value (such as NULL or asterisks) is shown.
This method enables fine-grained, data-driven masking policies while maintaining a single authoritative access mapping source.
Hardcoding values (A) reduces flexibility, and row filters (D) apply to entire rows rather than specific columns. Therefore, C correctly aligns with Databricks best practices for dynamic masking.

Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks follows the principle of least privilege (PoLP) to ensure users and groups have only the permissions required for their specific role.
For Databricks Jobs, three primary permission levels exist:
CAN VIEW - Allows viewing job configurations and runs.
CAN RUN - Allows triggering job runs.
CAN MANAGE - Allows full control, including editing and deletion.
To enforce PoLP, each user should be assigned only the specific permission required for their task. This approach prevents unauthorized edits and limits the risk of accidental deletions or job misconfigurations.
Granting broad permissions such as "ALL" or "CAN MANAGE to everyone" violates Databricks security best practices. Thus, the correct answer is D.