[Jan 27, 2026] Databricks-Certified-Data-Analyst-Associate Test Prep Training Practice Exam Questions Practice Tests [Q40-Q59]

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[Jan 27, 2026] Databricks-Certified-Data-Analyst-Associate Test Prep Training Practice Exam Questions Practice Tests

Exam Questions Answers Braindumps Databricks-Certified-Data-Analyst-Associate Exam Dumps PDF Questions

Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 2	Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint warehouses, trade-off between cluster size and cost for Databricks SQL endpoints warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
Topic 3	Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
Topic 4	Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.
Topic 5	Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.

NEW QUESTION 40
Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?

Visualization scale can be changed.

Data Labels can be formatted.

Colors can be changed.

Borders can be added.

Tooltips can be formatted.

NEW QUESTION 41
A data analyst needs to share a Databricks SQL dashboard with stakeholders that are not permitted to have accounts in the Databricks deployment. The stakeholders need to be notified every time the dashboard is refreshed.
Which approach can the data analyst use to accomplish this task with minimal effort/

By granting the stakeholders’ email addresses permissions to the dashboard

By adding the stakeholders’ email addresses to the refresh schedule subscribers list

By granting the stakeholders’ email addresses to the SQL Warehouse (formerly known as endpoint) subscribers list

By downloading the dashboard as a PDF and emailing it to the stakeholders each time it is refreshed

NEW QUESTION 42
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

Business analyst

SQL analyst

Data engineer

Business intelligence analyst

Data analyst

NEW QUESTION 43
Which of the following benefits of using Databricks SQL is provided by Data Explorer?

It can be used to run UPDATE queries to update any tables in a database.

It can be used to view metadata and data, as well as view/change permissions.

It can be used to produce dashboards that allow data exploration.

It can be used to make visualizations that can be shared with stakeholders.

It can be used to connect to third party Bl cools.

NEW QUESTION 44
Which of the following layers of the medallion architecture is most commonly used by data analysts?

None of these layers are used by data analysts

Gold

All of these layers are used equally by data analysts

Silver

Bronze

NEW QUESTION 45
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries and data visualizations. It is a requirement that the compute resources in the platform can be made serverless, and it is expected that data visualizations can be placed within a dashboard.
Which of the following Databricks Lakehouse Platform services/capabilities meets all of these requirements?

Delta Lake

Databricks Notebooks

Tableau

Databricks Machine Learning

Databricks SQL

NEW QUESTION 46
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
A data analyst has created a dashboard based on this gold level dat
a. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold-level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?

A refresh schedule with an interval of 10 minutes or less

A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint

A refresh schedule with stakeholders included as subscribers

A refresh schedule with a Structured Streaming cluster

NEW QUESTION 47
A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQL endpoint is taking too long to start up with each run.
Which of the following changes can the data analyst make to reduce the start-up time for the endpoint while managing costs?

Reduce the SQL endpoint cluster size

Increase the SQL endpoint cluster size

Turn off the Auto stop feature

Increase the minimum scaling value

Use a Serverless SQL endpoint

NEW QUESTION 48
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age >= 75 AND country = ‘canada’;
Which of the following tables represents the output of the above command?

NEW QUESTION 49
A data analyst has been asked to produce a visualization that shows the flow of users through a website.
Which of the following is used for visualizing this type of flow?

Heatmap

IChoropleth

Word Cloud

Pivot Table

Sankey

NEW QUESTION 50
A business analyst has been asked to create a data entity/object called sales_by_employee. It should always stay up-to-date when new data are added to the sales table. The new entity should have the columns sales_person, which will be the name of the employee from the employees table, and sales, which will be all sales for that particular sales person. Both the sales table and the employees table have an employee_id column that is used to identify the sales person.
Which of the following code blocks will accomplish this task?

NEW QUESTION 51
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.

Running SELECT * FROM accounts.customers will return all rows in the table.

All files with the .customers extension are deleted.

The accounts.customers table is removed from the metastore, and the underlying data files are deleted.

The accounts.customers table is removed from the metastore, but the underlying data files are untouched.

NEW QUESTION 52
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:

Which query did the analyst execute in order to get this result?

NEW QUESTION 53
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?

Copy the Query and create one data visualization per query.

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

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

Alter the Query to return two separate sets of results.

NEW QUESTION 54
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?

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

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

Edit the Owner field in the table page by selecting the new owner’s account

Edit the Owner field in the table page by removing all access

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

NEW QUESTION 55
What is used as a compute resource for Databricks SQL?

Single-node clusters

Downstream BI tools integrated with Databricks SQL

SQL warehouses

Standard clusters

NEW QUESTION 56
What describes Partner Connect in Databricks?

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

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

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

It is a feature that runs Databricks partner tools on a Databricks SQL Warehouse (formerly known as a SQL endpoint).

NEW QUESTION 57
Which of the following approaches can be used to ingest data directly from cloud-based object storage?

Create an external table while specifying the DBFS storage path to FROM

Create an external table while specifying the DBFS storage path to PATH

It is not possible to directly ingest data from cloud-based object storage

Create an external table while specifying the object storage path to FROM

Create an external table while specifying the object storage path to LOCATION

NEW QUESTION 58
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?

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.

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.

NEW QUESTION 59
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

DROP DATABASE database_name;

DELETE TABLE database_name.table_name;

DROP TABLE database_name.table_name;

DELETE TABLE table_name FROM database_name;

DROP TABLE table_name FROM database_name;

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[Jan 27, 2026] Databricks-Certified-Data-Analyst-Associate Test Prep Training Practice Exam Questions Practice Tests [Q40-Q59]

Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

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