Databricks Updated Databricks-Certified-Data-Analyst-Associate Exam Questions and Answers by libby

Databricks Partner Connect is designed to simplify and streamline the integration between Databricks and its technology partners. It provides a unified interface within the Databricks platform that facilitates the discovery and connection to a variety of data, analytics, and AI tools. By automating the configuration of necessary resources such as clusters, tokens, and connection files, Partner Connect enables seamless, bi-directional data flow between Databricks and partner solutions. This integration enhances the overall functionality of the Databricks Lakehouse by allowing users to easily incorporate external tools and services into their workflows, thereby expanding the platform's capabilities and fostering a more cohesive data ecosystem.​

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.

 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