Databricks Databricks Certified Data Analyst Associate Practice Exams
Last updated on Oct 21,2026- Exam Code: Databricks Certified Data Analyst Associate
- Exam Name: Databricks Certified Data Analyst Associate Exam
- Certification Provider: Databricks
- Latest update: Oct 21,2026
Question #41
What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?
- A . Computations, data, and analytical tools on the same platform
- B . Advanced dashboarding capabilities
- C . Simultaneous multi-user support
- D . Automated alerting systems
Correct Answer: A
A
Explanation:
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.
Reference: Databricks AI & BI: Transform Data into Actionable Insights
A
Explanation:
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.
Reference: Databricks AI & BI: Transform Data into Actionable Insights
Question #42
Which of the following layers of the medallion architecture is most commonly used by data analysts?
- A . None of these layers are used by data analysts
- B . Gold
- C . All of these layers are used equally by data analysts
- D . Silver
- E . Bronze
Correct Answer: B
B
Explanation:
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?
B
Explanation:
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?
Question #43
How can a data analyst determine if query results were pulled from the cache?
- A . Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.
- B . Go to the Alerts tab and check the Cache Status alert.
- C . Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
- D . Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
- E . Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
Correct Answer: A
A
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show “Cached”. If the result did not come from the cache, the cache status will show “Not cached”. You can also see the cache hit ratio, which is the percentage of queries that were served from the cache.
Reference: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL – Query Cache
A
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show “Cached”. If the result did not come from the cache, the cache status will show “Not cached”. You can also see the cache hit ratio, which is the percentage of queries that were served from the cache.
Reference: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL – Query Cache
Question #44
What describes Partner Connect in Databricks?
- A . it allows for free use of Databricks partner tools through a common API.
- B . it allows multi-directional connection between Databricks and Databricks partners easier.
- C . It exposes connection information to third-party tools via Databricks partners.
- D . It is a feature that runs Databricks partner tools on a Databricks SQL Warehouse (formerly known as a SQL endpoint).
Correct Answer: B
B
Explanation:
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
Reference: Discover Databricks Partner Connect
B
Explanation:
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
Reference: Discover Databricks Partner Connect
Question #45
A stakeholder has provided a data analyst with a lookup dataset in the form of a 50-row CSV file. The data analyst needs to upload this dataset for use as a table in Databricks SQL.
Which approach should the data analyst use to quickly upload the file into a table for use in Databricks SOL?
- A . Create a table by uploading the file using the Create page within Databricks SQL
- B . Create a table via a connection between Databricks and the desktop facilitated by Partner Connect.
- C . Create a table by uploading the file to cloud storage and then importing the data to Databricks.
- D . Create a table by manually copying and pasting the data values into cloud storage and then
importing the data to Databricks.
Correct Answer: A
A
Explanation:
Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows: Access the Data Upload Interface:
In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.
Select Create or modify a table.
Upload the CSV File:
Click on the browse button or drag and drop the CSV file directly onto the designated area. The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB. Configure Table Settings:
After uploading, a preview of the data is displayed.
Specify the table name, select the appropriate schema, and configure any additional settings as needed.
Create the Table:
Once all configurations are set, click on the Create Table button to finalize the process.
This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.
Reference: Create or modify a table using file upload
A
Explanation:
Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows: Access the Data Upload Interface:
In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.
Select Create or modify a table.
Upload the CSV File:
Click on the browse button or drag and drop the CSV file directly onto the designated area. The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB. Configure Table Settings:
After uploading, a preview of the data is displayed.
Specify the table name, select the appropriate schema, and configure any additional settings as needed.
Create the Table:
Once all configurations are set, click on the Create Table button to finalize the process.
This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.
Reference: Create or modify a table using file upload
Question #46
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?
A) CREATE TABLE table_silver AS SELECT DISTINCT *
FROM table_bronze;
B) CREATE TABLE table_silver AS
INSERT *
FROM table_bronze;
C) CREATE TABLE table_silver AS
MERGE DEDUPLICATE *
FROM table_bronze;
D) INSERT INTO TABLE table_silver
SELECT * FROM table_bronze;
E) INSERT OVERWRITE TABLE table_silver
SELECT * FROM table_bronze;
- A . Option A
- B . Option B
- C . Option C
- D . Option D
- E . Option E
Correct Answer: A
A
Explanation:
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data
from table_bronze, without removing any duplicates.
Reference: Delete Duplicate using SPARK SQL, Spark SQL – How to Remove Duplicate Rows
A
Explanation:
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data
from table_bronze, without removing any duplicates.
Reference: Delete Duplicate using SPARK SQL, Spark SQL – How to Remove Duplicate Rows
Question #47
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?
- A . ACID transactions
- B . Flexible schemas
- C . Data deletion
- D . Scalable storage
- E . Open-source formats
Correct Answer: A
A
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions.
Reference: Delta Lake: Lakehouse, warehouse, advantages | Definition Synapse C Data Lake vs. Delta Lake vs. Data Lakehouse Data Lake vs. Delta Lake – A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide
A
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions.
Reference: Delta Lake: Lakehouse, warehouse, advantages | Definition Synapse C Data Lake vs. Delta Lake vs. Data Lakehouse Data Lake vs. Delta Lake – A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide
Question #48
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?
- A . Reduce the SQL endpoint cluster size
- B . Increase the SQL endpoint cluster size
- C . Turn off the Auto stop feature
- D . Increase the minimum scaling value
- E . Use a Serverless SQL endpoint
Correct Answer: E
E
Explanation:
A Serverless SQL endpoint is a type of SQL endpoint that does not require a dedicated cluster to run queries. Instead, it uses a shared pool of resources that can scale up and down automatically based on the demand. This means that a Serverless SQL endpoint can start up much faster than a SQL
endpoint that uses a cluster, and it can also save costs by only paying for the resources that are used. A Serverless SQL endpoint is suitable for ad-hoc queries and exploratory analysis, but it may not offer the same level of performance and isolation as a SQL endpoint that uses a cluster. Therefore, a data analyst should consider the trade-offs between speed, cost, and quality when choosing between a Serverless SQL endpoint and a SQL endpoint that uses a cluster.
Reference: Databricks SQL endpoints, Serverless SQL endpoints, SQL endpoint clusters
E
Explanation:
A Serverless SQL endpoint is a type of SQL endpoint that does not require a dedicated cluster to run queries. Instead, it uses a shared pool of resources that can scale up and down automatically based on the demand. This means that a Serverless SQL endpoint can start up much faster than a SQL
endpoint that uses a cluster, and it can also save costs by only paying for the resources that are used. A Serverless SQL endpoint is suitable for ad-hoc queries and exploratory analysis, but it may not offer the same level of performance and isolation as a SQL endpoint that uses a cluster. Therefore, a data analyst should consider the trade-offs between speed, cost, and quality when choosing between a Serverless SQL endpoint and a SQL endpoint that uses a cluster.
Reference: Databricks SQL endpoints, Serverless SQL endpoints, SQL endpoint clusters
Question #49
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?
- A . They will need to alter the Query to return two separate sets of results.
- B . They will need to add two separate visualizations to the dashboard based on the same Query.
- C . They will need to create two separate dashboards.
- D . They will need to decide on a single data visualization to add to the dashboard.
- E . They will need to copy the Query and create one data visualization per query.
Correct Answer: B
B
Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard.
Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL
B
Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard.
Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL
Question #50
A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
Which of the following terms is used to describe this type of work?
- A . Data blending
- B . Last-mile
- C . Data testing
- D . Last-mile ETL
- E . Data enhancement
Correct Answer: D
D
Explanation:
Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning.
Reference: Databricks – Last-mile ETL, Databricks – Data Analysis with Databricks SQL
D
Explanation:
Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning.
Reference: Databricks – Last-mile ETL, Databricks – Data Analysis with Databricks SQL