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

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.”

 Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. References: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:​

Develop Queries:

For each desired visualization or counter, write a SQL query that retrieves the necessary data.​

Create Visualizations and Counters:

After executing each query, utilize the results to create corresponding visualizations or counters. Databricks SQL offers a variety of visualization types to represent data effectively.​

Assemble the Dashboard:

Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.​

By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.​