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

Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table , the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly. External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files . Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.

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he approach that can be used to identify the owner of new_table is to review the Owner field in the table’s page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table’s page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.

The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table’s page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table’s page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5. There is a way to identify the owner of the table, as explained above, so option E is false.

1: Data Explorer | Databricks on AWS

2: Table details | Databricks on AWS

3: Set owner when creating a view in databricks sql - Databricks - 9978

4: Table access control | Databricks on AWS

5: External tables | Databricks on AWS