Databricks-Certified-Data-Analyst-Associate Exam Dumps - Databricks Data Analyst Questions and Answers

 Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data from table_bronze, without removing any duplicates. References: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows

In Databricks, to determine the owner of a managed table, you can utilize the Catalog Explorer feature. The steps are as follows:​

Access Catalog Explorer:

In your Databricks workspace, click on theCatalogicon in the sidebar to open Catalog Explorer.​

Navigate to the Table:

Within Catalog Explorer, browse through the catalog and schema to locate the specific managed table whose ownership you wish to verify.​

View Table Details:

Click on the table name to open its details page.​

Identify the Owner:

On the table's details page, review theOwnerfield, which displays the principal (user, service principal, or group) that owns the table.​

This method provides a straightforward way to ascertain the ownership of managed tables within the Databricks environment. Understanding table ownership is essential for managing permissions and ensuring proper access control.​

 A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses “Dashboard Parameter” for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. References: Databricks SQL dashboards, Query parameters

Based on the images you sent, the two statements are SQL queries for different types of joins between the customers and orders tables. A join is a way of combining the rows from two table references based on some criteria. The join type determines how the rows are matched and what kind of result set is returned. The first statement is a query for a LEFT SEMI JOIN, which returns only the rows from the left table reference (customers) that have a match with the right table reference (orders) on the join condition (customer_id). The second statement is a query for a LEFT ANTI JOIN, which returns only the rows from the left table reference (customers) that have no match with the right table reference (orders) on the join condition (customer_id). Therefore, the result sets for the two statements will differ in the following way:

The first statement will return a subset of the customers table that contains only the customers who have placed at least one order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT SEMI JOIN does not include any columns from the orders table.

The second statement will return a subset of the customers table that contains only the customers who have not placed any order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have no orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT ANTI JOIN does not include any columns from the orders table.

The other options are not correct because:

A. The first statement will not return all data from the customers table, as it will exclude the customers who have no orders. The second statement will not return all data from the orders table, as it will exclude the orders that have a matching customer. Neither statement will fill in any missing data with NULL, as they do not return any columns from the other table.

C. There is a difference between the result sets for both statements, as explained above. The LEFT SEMI JOIN and the LEFT ANTI JOIN are not equivalent operations and will produce different outputs.

D. Both statements will not fail, as Databricks SQL does support those join types. Databricks SQL supports various join types, including INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, LEFT SEMI, LEFT ANTI, and CROSS. You can also use NATURAL, USING, or LATERAL keywords to specify different join criteria.

E. The first statement will not return only the customer_id from the orders table, as it will return all columns from the customers table. The second statement is correct, but it is not the only difference between the result sets.

The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results. References: Databricks - DROP TABLE

Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:​

Access the Data Upload Interface:

In the Databricks workspace, navigate to the sidebar and click onNew>Add or upload data.​

SelectCreate or modify a table.​

Upload the CSV File:

Click on thebrowsebutton or drag and drop the CSV file directly onto the designated area.​

The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.​

Configure Table Settings:

After uploading, a preview of the data is displayed.​

Specify the table name, select the appropriate schema, and configure any additional settings as needed.​

Create the Table:

Once all configurations are set, click on theCreate Tablebutton to finalize the process.​

This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.​

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.​

In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.

The result set provided shows a combination of grouping by two columns (group_1andgroup_2) with subtotals for each level of grouping and a grand total. This pattern is typical of aGROUP BY ... WITH ROLLUPoperation in SQL, which provides subtotal rows and a grand total row in the result set.

Considering the query options:

A)Option A:GROUP BY group_1, group_2 INCLUDING NULL- This is not a standard SQL clause and would not result in subtotals and a grand total.

B)Option B:GROUP BY group_1, group_2 WITH ROLLUP- This would create subtotals for each uniquegroup_1, each combination ofgroup_1andgroup_2, and a grand total, which matches the result set provided.

C)Option C:GROUP BY group_1, group 2- This is a simpleGROUP BYand would not include subtotals or a grand total.

D)Option D:GROUP BY group_1, group_2, (group_1, group_2)- This syntax is not standard and would likely result in an error or be interpreted as a simpleGROUP BY, not providing the subtotals and grand total.

E)Option E:GROUP BY group_1, group_2 WITH CUBE- TheWITH CUBEoperation produces subtotals for all combinations of the selected columns and a grand total, which is more than what is shown in the result set.

The correct answer isOption B, which usesWITH ROLLUPto generate the subtotals for each level of grouping as well as a grand total. This matches the result set where we have subtotals for eachgroup_1, each combination ofgroup_1andgroup_2, and the grand total where bothgroup_1andgroup_2areNULL.

A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. References: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL