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Databricks Certified Data Analyst Associate Exam Questions and Answers

Questions 1

Consider the following two statements:

Statement 1:

Statement 2:

Which of the following describes how the result sets will differ for each statement when they are run in Databricks SQL?

Options:

The first statement will return all data from the customers table and matching data from the orders table. The second statement will return all data from the orders table and matching data from the customers table. Any missing data will be filled in with NULL.

When the first statement is run, only rows from the customers table that have at least one match with the orders table on customer_id will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.

There is no difference between the result sets for both statements.

Both statements will fail because Databricks SQL does not support those join types.

When the first statement is run, all rows from the customers table will be returned and only the customer_id from the orders table will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.

Answer:

Explanation:

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.

References: JOIN | Databricks on AWS, JOIN - Azure Databricks - Databricks SQL | Microsoft Learn, array_join function | Databricks on AWS, Hints | Databricks on AWS

Questions 2

Which of the following statements about a refresh schedule is incorrect?

Options:

A query can be refreshed anywhere from 1 minute lo 2 weeks

Refresh schedules can be configured in the Query Editor.

A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).

A refresh schedule is not the same as an alert.

You must have workspace administrator privileges to configure a refresh schedule

Questions 3

A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform.

Which of the following projects should be completed in Databricks SQL?

Options:

Testing the quality of data as it is imported from a source

Tracking usage of feature variables for machine learning projects

Combining two data sources into a single, comprehensive dataset

Segmenting customers into like groups using a clustering algorithm

Automatingcomplex notebook-based workflows with multiple tasks

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