Databricks Databricks-Certified-Data-Engineer-Associate Exam With Confidence Using Practice Dumps

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.

The OPTIMIZE command in Delta Lake is designed to compact small files into larger files to improve query performance and reduce file system overhead. Importantly, OPTIMIZE is considered an idempotent operation when run on unchanged data. This means that once files have been compacted optimally, running OPTIMIZE again does not produce additional changes unless new small files have been added to the table. Databricks ensures that file compaction is efficient and avoids unnecessary rewrites, which helps control compute costs and maintain stable performance. While features like ZORDER can be applied during OPTIMIZE to improve data skipping, re-running the same command without new data will not further reorganize files. Options A, B, and C incorrectly imply additional restructuring or changes on repeated execution. According to Delta Lake best practices, OPTIMIZE should be run periodically after data ingestion events rather than repeatedly on unchanged datasets. This behavior reinforces predictable performance and efficient resource utilization in Databricks environments.

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In Unity Catalog, an external table is defined by referencing data that already exists in cloud storage, and Databricks documentation states that this is done by using a LOCATION clause that points to the storage path. Databricks also notes that the external table’s path should be contained within an existing external location that has been configured with the appropriate storage credential and access permissions. This makes option A the correct choice. Option B is incorrect because simply calling something “unmanaged” without specifying a location does not create a proper Unity Catalog external table. Option C is also incorrect because Databricks documentation explicitly requires a storage location for Unity Catalog external tables. Option D is wrong because managed tables are intended for Databricks-managed storage and lifecycle handling, not for referencing existing external files. So, for ADLS-based data that must remain in place while still being queryable through Databricks, the right pattern is to configure the external location and then create the table with a CREATE TABLE ... LOCATION statement.

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