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

The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system.

Option B is not correct, as the size of the table’s data does not affect the behavior of dropping the table. Whether the table’s data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external.

Option C is not correct, for the same reason as option B.

Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system.

Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.

Managing Tables

[Databricks Data Engineer Professional Exam Guide]

 To grant full permissions on a database to a user, group, or service principal, the GRANT ALL PRIVILEGES ON DATABASE command can be used. This command grants all the applicable privileges on the database, such as CREATE, SELECT, MODIFY, and USAGE. The other options are either incorrect or incomplete, as they do not grant all the privileges or specify the wrong database or principal. References:

GRANT

Privileges

Option B reads the relational source through JDBC and writes the resulting Spark DataFrame directly as a registered Unity Catalog table using its three-level name: catalog, schema, and table. Once written with saveAsTable, the data is immediately discoverable and queryable through Databricks SQL, subject to Unity Catalog permissions. Because the read and write operations occur through supported Databricks workloads, Unity Catalog can capture lineage between the source operation, notebook or job, and destination table. Writing files only to DBFS or an external path does not by itself create the required governed table. Introducing a separate staging and loading job adds unnecessary processing and operational complexity. A Pandas extraction also reduces scalability and does not satisfy the direct managed-table requirement. Therefore, option B is correct.

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