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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 Apr 18, 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

A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.

Which of the following approaches can the analyst use to complete the task?

AEdit the Owner field in the table page by removing their own account

BEdit the Owner field in the table page by selecting All Users

CEdit the Owner field in the table page by selecting the new owner's account

DEdit the Owner field in the table page by selecting the Admins group

EEdit the Owner field in the table page by removing all access
The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list.This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:
A .Removing the owner's account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
B .Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
D .Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
E .Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4.Reference:
1: Change table ownership
2: Ownerless tables
3: Table access control
4: Revoke access to a table

Show Answer

Correct Answer: C

Question No. 3

Delta Lake stores table data as a series of data files, but it also stores a lot of other information.

Which of the following is stored alongside data files when using Delta Lake?

ANone of these

BTable metadata, data summary visualizations, and owner account information

CTable metadata

DData summary visualizations

EOwner account information
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
Table schema
Partitioning information
Data file paths
Transactional operations like inserts, updates, and deletes
Commit history and version control
This metadata is critical for supporting Delta Lake's advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.

Show Answer

Correct Answer: C

Question No. 4

Which of the following layers of the medallion architecture is most commonly used by data analysts?

ANone of these layers are used by data analysts

BGold

CAll of these layers are used equally by data analysts

DSilver

EBronze
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse.Reference:What is the medallion lakehouse architecture?

Show Answer

Correct Answer: B

Question No. 5

Which statement about subqueries is correct?

ASubqueries are not available in Databricks SQL

BSubqueries can be used like other user-defined functions to transform data into different data types.

CSubqueries can retrieve data without requiring the creation of a table or view.

DSubqueries can be used like other built-in functions to transform data into different data types.
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.

Show Answer

Correct Answer: C