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Free Databricks Databricks-Certified-Data-Analyst-Associate Exam Actual Questions

The questions for Databricks-Certified-Data-Analyst-Associate were last updated On Jun 12, 2025

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Question No. 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?

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.

Show Answer

Correct Answer: B

Question No. 2

Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?

AAs an exact substitute with the same level of functionality

BAs a substitute with less functionality

CAs a complete replacement with additional functionality

DAs a complementary tool for professional-grade presentations

EAs a complementary tool for quick in-platform Bl work
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse.Reference:Databricks SQL,Connecting Applications and BI Tools to Databricks SQL,Databricks integrations overview,Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

Show Answer

Correct Answer: E

Question No. 3

A data analyst runs the following command:

INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;

What is the result of running this command?

AThe suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.

BThe command fails because it is written incorrectly.

CThe suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.

DThe suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.

EThe suppliers table now contains only the data from the new suppliers table.
The commandINSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliersis not a valid syntax for inserting data into a table in Databricks SQL.According to the documentation12, the correct syntax for inserting data into a table is either:
INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query
INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query
The command in the question is missing theOVERWRITEorINTOkeyword, and thequerypart that specifies the source of the data to be inserted. TheTABLEkeyword is optional and can be omitted. ThePARTITIONclause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.

Show Answer

Correct Answer: B

INSERT | Databricks on AWS

INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

Question No. 4

Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

AIt has increased customization capabilities

BIt is easy to migrate existing SQL queries to Databricks SQL

CIt allows for the use of Photon's computation optimizations

DIt is more performant than other SQL dialects

EIt is more compatible with Spark's interpreters
Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL specifications defined by the American National Standards Institute (ANSI). This makes it easier to migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a similar dialect, such as PostgreSQL, Oracle, or Teradata.By using ANSI SQL, Databricks SQL avoids surprises in behavior or unfamiliar syntax that may arise from using a non-standard SQL dialect, such as Spark SQL or Hive SQL12.Moreover, Databricks SQL also adds compatibility features to support common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and user-defined functions2.Reference:ANSI compliance in Databricks Runtime,Evolution of the SQL language at Databricks: ANSI standard by default and easier migrations from data warehouses

Show Answer

Correct Answer: B

Question No. 5

A data analysis team is working with the table_bronze SQL table as a source for one of its most complex projects. A stakeholder of the project notices that some of the downstream data is duplicative. The analysis team identifies table_bronze as the source of the duplication.

Which of the following queries can be used to deduplicate the data from table_bronze and write it to a new table table_silver?

CREATE TABLE table_silver AS

SELECT DISTINCT *

FROM table_bronze;

CREATE TABLE table_silver AS

INSERT *

FROM table_bronze;

CREATE TABLE table_silver AS

MERGE DEDUPLICATE *

FROM table_bronze;

INSERT INTO TABLE table_silver

SELECT * FROM table_bronze;

INSERT OVERWRITE TABLE table_silver

SELECT * FROM table_bronze;

AOption A

BOption B

COption C

DOption D

EOption E
Option A uses theSELECT DISTINCTstatement to remove duplicate rows from thetable_bronzeand create a new tabletable_silverwith the deduplicated data.This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows fromtable_bronzeintotable_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is noMERGE DEDUPLICATEstatement. Option D appends all the rows fromtable_bronzeintotable_silver, without removing any duplicates. Option E overwrites the existing data intable_silverwith the data fromtable_bronze, without removing any duplicates.Reference:Delete Duplicate using SPARK SQL,Spark SQL - How to Remove Duplicate Rows

Show Answer

Correct Answer: A