Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column "employees". It applies a lambda function i - > i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column "exp_employees".
The use of higher-order functions like FILTER can be referenced from Databricks documentation on Higher- Order Functions.

Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

Lakehouse Federation is the Databricks feature built for querying external systems without moving all data into Databricks . Databricks documentation describes it as the platform for query federation , enabling users to run queries against multiple external data sources while keeping governance centralized through Unity Catalog . Databricks also documents support for external systems such as Amazon Redshift and other databases through connections and foreign catalogs, allowing read-only access to external data while managing permissions in Unity Catalog. This aligns directly with the requirement to minimize duplication and still maintain centralized governance. Databricks Connect is for local development against Databricks compute, not federated querying. MLflow is for machine learning lifecycle management. Delta Lake is a storage format and table layer, not a federation framework. Therefore, when the goal is unified governance across Databricks and external systems like MySQL, Redshift, and BigQuery without first ingesting the data, Lakehouse Federation is the documented answer. Databricks does note that for high-volume production ingestion, managed connectors may sometimes be preferred, but for direct querying without data movement, Lakehouse Federation is the correct feature.

The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving. References: Workspace access control, Manage Unity Catalog object ownership.