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

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.”

Databricks SQL offers a unified platform where computations, data storage, and analytical tools coexist seamlessly. This integration allows business intelligence (BI) analytics projects to be executed more efficiently, as users can perform data processing and analysis without the need to transfer data between disparate systems. By consolidating these components, Databricks SQL streamlines workflows, reduces latency, and enhances data governance. While third-party BI tools may offer advanced dashboarding capabilities, simultaneous multi-user support, and automated alerting systems, they often require integration with separate data processing platforms, which can introduce complexity and potential inefficiencies.​

Option C is the correct answer because it creates a new aggregated table, store_sales, from the existing cleaned/refined table cleaned_transactions. In a medallion architecture, a table such as cleaned_transactions represents a Silver-layer table because it contains cleaned or validated transaction-level data. The query then creates store_sales by grouping by store_id and calculating SUM(sales), which is an aggregation suitable for analytics and reporting. That is exactly the Silver-to-Gold hop.

Option A creates cleaned_transactions from transactions, so it is moving toward a cleaned Silver table, not creating a Gold table. Option B creates transactions from raw_transactions, which is closer to a raw-to-Bronze or Bronze preparation step. Option D filters rows from transactions into cleaned_transactions, which is also a cleaning/validation step associated with Silver, not Gold.

Exact extract from official Databricks documentation: Databricks describes medallion architecture as improving data through “Bronze ⇒ Silver ⇒ Gold layer tables.” The same official page identifies Silver as “Data cleaning and validation” and Gold as “Dimensional modeling and aggregation.” Databricks also states that the Gold layer “consists of aggregated data tailored for analytics and reporting.”