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 #11
Which of the following statements describes descriptive statistics?
- A . A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
- B . A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.
- C . A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.
- D . A branch of statistics that uses summary statistics to categorically describe and summarize data.
- E . A branch of statistics that uses quantitative variables that must take on an uncountable set of values.
Correct Answer: A
A
Explanation:
Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data. Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of
data.
Reference: Databricks – Descriptive Statistics, Databricks – Data Analysis with Databricks SQL
A
Explanation:
Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data. Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of
data.
Reference: Databricks – Descriptive Statistics, Databricks – Data Analysis with Databricks SQL
Question #12
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 #13
In which of the following situations will the mean value and median value of variable be meaningfully different?
- A . When the variable contains no outliers
- B . When the variable contains no missing values
- C . When the variable is of the boolean type
- D . When the variable is of the categorical type
- E . When the variable contains a lot of extreme outliers
Correct Answer: E
E
Explanation:
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1.
Reference: Difference Between Mean and Median in Statistics (With Example) – BYJU’S
E
Explanation:
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1.
Reference: Difference Between Mean and Median in Statistics (With Example) – BYJU’S
Question #14
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 #15
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?
- A . None of these
- B . Table metadata, data summary visualizations, and owner account information
- C . Table metadata
- D . Data summary visualizations
- E . Owner account information
Correct Answer: C
C
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.
Reference: Delta Lake Table Features – Databricks Documentation
C
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.
Reference: Delta Lake Table Features – Databricks Documentation
Question #16
A data analyst has created a user-defined function using the following line of code:
CREATE FUNCTION price(spend DOUBLE, units DOUBLE)
RETURNS DOUBLE
RETURN spend / units;
Which of the following code blocks can be used to apply this function to the customer_spend and customer_units columns of the table customer_summary to create column customer_price?
- A . SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary
- B . SELECT price FROM customer_summary
- C . SELECT function(price(customer_spend, customer_units)) AS customer_price FROM
customer_summary - D . SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
- E . SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary
Correct Answer: E
E
Explanation:
A user-defined function (UDF) is a function defined by a user, allowing custom logic to be reused in the user environment1. To apply a UDF to a table, the syntax is SELECT udf_name(column_name) AS alias FROM table_name2. Therefore, option E is the correct way to use the UDF price to create a new
column customer_price based on the existing columns customer_spend and customer_units from the table customer_summary.
Reference: What are user-defined functions (UDFs)?
User-defined scalar functions – SQL
V
E
Explanation:
A user-defined function (UDF) is a function defined by a user, allowing custom logic to be reused in the user environment1. To apply a UDF to a table, the syntax is SELECT udf_name(column_name) AS alias FROM table_name2. Therefore, option E is the correct way to use the UDF price to create a new
column customer_price based on the existing columns customer_spend and customer_units from the table customer_summary.
Reference: What are user-defined functions (UDFs)?
User-defined scalar functions – SQL
V
Question #17
Data professionals with varying responsibilities use the Databricks Lakehouse Platform.
Which role in the Databricks Lakehouse Platform use Databricks SQL as their primary service?
- A . Data scientist
- B . Data engineer
- C . Platform architect
- D . Business analyst
Correct Answer: D
D
In the Databricks Lakehouse Platform, business analysts primarily utilize Databricks SQL as their main service. Databricks SQL provides an environment tailored for executing SQL queries, creating visualizations, and developing dashboards, which aligns with the typical responsibilities of business analysts who focus on interpreting data to inform business decisions. While data scientists and data engineers also interact with the Databricks platform, their primary tools and services differ; data scientists often engage with machine learning frameworks and notebooks, whereas data engineers focus on data pipelines and ETL processes. Platform architects are involved in designing and overseeing the infrastructure and architecture of the platform. Therefore, among the roles listed, business analysts are the primary users of Databricks SQL.
Reference: The scope of the lakehouse platform
D
In the Databricks Lakehouse Platform, business analysts primarily utilize Databricks SQL as their main service. Databricks SQL provides an environment tailored for executing SQL queries, creating visualizations, and developing dashboards, which aligns with the typical responsibilities of business analysts who focus on interpreting data to inform business decisions. While data scientists and data engineers also interact with the Databricks platform, their primary tools and services differ; data scientists often engage with machine learning frameworks and notebooks, whereas data engineers focus on data pipelines and ETL processes. Platform architects are involved in designing and overseeing the infrastructure and architecture of the platform. Therefore, among the roles listed, business analysts are the primary users of Databricks SQL.
Reference: The scope of the lakehouse platform
Question #18
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 #19
A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production. They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?
- A . Separate endpoints for each section
- B . Separate queries for each section
- C . Markdown-based text boxes
- D . Direct text written into the dashboard in editing mode
- E . Separate color palettes for each section
Correct Answer: C
C
Explanation:
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard
icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option.
Reference: Dashboards in notebooks, How to add text to a dashboard in Databricks
C
Explanation:
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard
icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option.
Reference: Dashboards in notebooks, How to add text to a dashboard in Databricks
Question #20
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