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The correct answer is C because a dashboard is designed to collect multiple visualizations and query outputs in one shared reporting interface. A SQL warehouse provides compute for running SQL queries, but it is not a sharing/reporting interface. Query History only shows past query executions. A visualization tab belongs to one query result, while a dashboard can display multiple results together for stakeholders.

Official documentation extract used: Databricks states that dashboards are used to “build data visualizations and share reports with your team.”

When connecting Power BI and Tableau through Databricks Partner Connect, the system automates several steps to streamline the integration process:​

Personal Access Token Creation: Partner Connect generates a Databricks personal access token, which is essential for authenticating and establishing a secure connection between Databricks and the BI tools.​

ODBC Driver Installation: The appropriate ODBC driver is downloaded and installed. This driver facilitates communication between the BI tools and Databricks, ensuring compatibility and optimal performance.​

Configuration File Download: A configuration file tailored for the selected BI tool (Power BI or Tableau) is provided. This file contains the necessary connection details, simplifying the setup process within the BI tool.​

By automating these steps, Partner Connect ensures a seamless and efficient integration, reducing manual configuration efforts and potential errors.​

Option B is correct. PIVOT rotates unique values from rows into separate columns, which is exactly the process of converting long-format data into wide-format data. UNPIVOT does the reverse. SUM is an aggregate function, WHERE filters rows, and TRANSFORM is not the SQL keyword used for this reshaping task. Official Databricks extract: the PIVOT clause “rotat[es] unique values from a column into separate columns.”

The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:

INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query

INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query

The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.

INSERT | Databricks on AWS

INSERT - Azure Databricks - Databricks SQL | Microsoft Learn