Important Databricks Certified Data Analyst Associate Exam Questions

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Databricks Certified Data Analyst Associate Exam

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Exam Name:	Databricks Certified Data Analyst Associate Exam
Registration Code:	Databricks-Certified-Data-Analyst-Associate
Related Certification:	Databricks Certified Data Analyst Certification
Exam Audience:	Aspiring Data Analysts and Business Intelligence Professionals,

Total Questions

Last Updated

28-08-2025

Exam Duration

90 MINUTES

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Question: 1

Consider the following two statements:

Statement 1:

Exam Question 1 Exhibit 1

Statement 2:

Exam Question 1 Exhibit 2

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

AThe 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.

BWhen 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.

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

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

EWhen 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.
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.

Answer: B

Explanation:

Question: 2

A data analyst has created a Query in Databricks SQL, and now wants to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.

Which step will the data analyst need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

ACopy the Query and create one data visualization per query.

BAdd two separate visualizations to the dashboard based on the same Query.

CDecide on a single data visualization to add to the dashboard.

DAlter the Query to return two separate sets of results.

Answer: B

Explanation:

Question: 3

What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?

AComputations, data, and analytical tools on the same platform

BAdvanced dashboarding capabilities

CSimultaneous multi-user support

DAutomated alerting systems
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.

Answer: A

Explanation:

Question: 4

Which statement describes descriptive statistics?

AA branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.

BA branch of statistics that uses summary statistics to categorically describe and summarize data.

CA branch of statistics that uses summary statistics to quantitatively describe and summarize data.

DA branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.

Answer: C

Explanation:

Question: 5

What describes Partner Connect in Databricks?

Ait allows for free use of Databricks partner tools through a common API.

Bit allows multi-directional connection between Databricks and Databricks partners easier.

CIt exposes connection information to third-party tools via Databricks partners.

DIt is a feature that runs Databricks partner tools on a Databricks SQL Warehouse (formerly known as a SQL endpoint).
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.https://www.databricks.com/blog/2021/11/18/now-generally-available-introducing-databricks-partner-connect-to-discover-and-connect-popular-data-and-ai-tools-to-the-lakehouse?utm_source=chatgpt.com

Answer: B

Explanation:

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