Databricks-Certified-Data-Engineer-Associate Exam Dumps : Databricks Certified Data Engineer Associate Exam

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

The --target prod option explicitly selects the prod target from the bundle configuration. Databricks then resolves ${var.dataset_catalog} using the target-level value catalog_prod, which takes precedence over the top-level default of catalog_dev. This allows the same dashboard resource definition to be promoted across environments without editing the resource itself. The --var option can override an individual variable, but it does not automatically select the production target. The --profile option selects an authentication profile from .databrickscfg; it does not select a bundle target or automatically apply the target’s variables. bundle execute is not the deployment command. Consequently, option D correctly combines production-target selection with the environment-specific variable override already defined in the YAML configuration.

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