Practice Free Databricks Certified Data Analyst Associate Exam Online Questions
Question #1
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?
- A . As an exact substitute with the same level of functionality
- B . As a substitute with less functionality
- C . As a complete replacement with additional functionality
- D . As a complementary tool for professional-grade presentations
- E . As a complementary tool for quick in-platform Bl work
Correct Answer: E
E
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
E
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
Question #2
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 #3
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 #4
In which of the following situations should a data analyst use higher-order functions?
- A . When custom logic needs to be applied to simple, unnested data
- B . When custom logic needs to be converted to Python-native code
- C . When custom logic needs to be applied at scale to array data objects
- D . When built-in functions are taking too long to perform tasks
- E . When built-in functions need to run through the Catalyst Optimizer
Correct Answer: C
C
Explanation:
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions.
Reference: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions – Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS
C
Explanation:
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions.
Reference: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions – Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS
Question #5
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?
- A . Heatmap
- B . IChoropleth
- C . Word Cloud
- D . Pivot Table
- E . Sankey
Correct Answer: E
E
A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories. It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization.
Reference: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics – Sankey Diagram, Databricks Visualizations – Sankey Diagram
E
A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories. It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization.
Reference: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics – Sankey Diagram, Databricks Visualizations – Sankey Diagram
Question #6
A data engineer is working with a nested array column products in table transactions. They want to expand the table so each unique item in products for each row has its own row where the transaction_id column is duplicated as necessary.
They are using the following incomplete command:
Which of the following lines of code can they use to fill in the blank in the above code block so that it successfully completes the task?
- A . array distinct(produces)
- B . explode(produces)
- C . reduce(produces)
- D . array(produces)
- E . flatten(produces)
Correct Answer: B
B
The explode function is used to transform a DataFrame column of arrays or maps into multiple rows, duplicating the other column’s values. In this context, it will be used to expand the nested array column products in the transactions table so that each unique item in products for each row has its own row and the transaction_id column is duplicated as necessary.
Reference: Databricks Documentation
I also noticed that you sent me an image along with your message. The image shows a snippet of SQL code that is incomplete. It begins with “SELECT” indicating a query to retrieve data. “transaction_id,” suggests that transaction_id is one of the columns being selected. There are blanks indicated by underscores where certain parts of the SQL command should be, including what appears to be an alias for a column and part of the FROM clause. The query ends with “FROM transactions;” indicating data is being selected from a ‘transactions’ table.
If you are interested in learning more about Databricks Data Analyst Associate certification, you can check out the following resources:
Databricks Certified Data Analyst Associate: This is the official page for the certification exam, where
you can find the exam guide, registration details, and preparation tips.
Data Analysis With Databricks SQL: This is a self-paced course that covers the topics and skills required for the certification exam. You can access it for free on Databricks Academy.
Tips for the Databricks Certified Data Analyst Associate Certification: This is a blog post that provides some useful advice and study tips for passing the certification exam.
Databricks Certified Data Analyst Associate Certification: This is another blog post that gives an overview of the certification exam and its benefits.
B
The explode function is used to transform a DataFrame column of arrays or maps into multiple rows, duplicating the other column’s values. In this context, it will be used to expand the nested array column products in the transactions table so that each unique item in products for each row has its own row and the transaction_id column is duplicated as necessary.
Reference: Databricks Documentation
I also noticed that you sent me an image along with your message. The image shows a snippet of SQL code that is incomplete. It begins with “SELECT” indicating a query to retrieve data. “transaction_id,” suggests that transaction_id is one of the columns being selected. There are blanks indicated by underscores where certain parts of the SQL command should be, including what appears to be an alias for a column and part of the FROM clause. The query ends with “FROM transactions;” indicating data is being selected from a ‘transactions’ table.
If you are interested in learning more about Databricks Data Analyst Associate certification, you can check out the following resources:
Databricks Certified Data Analyst Associate: This is the official page for the certification exam, where
you can find the exam guide, registration details, and preparation tips.
Data Analysis With Databricks SQL: This is a self-paced course that covers the topics and skills required for the certification exam. You can access it for free on Databricks Academy.
Tips for the Databricks Certified Data Analyst Associate Certification: This is a blog post that provides some useful advice and study tips for passing the certification exam.
Databricks Certified Data Analyst Associate Certification: This is another blog post that gives an overview of the certification exam and its benefits.
Question #7
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?
- A . Alerts don’t work with queries that access tables.
- B . Queries that return results based on dates cannot be used with Alerts.
- C . The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
- D . Queries that use query parameters cannot be used with Alerts.
- E . The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
Correct Answer: D
D
Explanation:
The reason the alert is not functioning as expected is because Databricks SQL Alerts do not support query parameters. This limitation applies to all types of parameters, including date parameters.
Here’s why:
Alerts require static, deterministic query results so they can compare values consistently during scheduled executions.
When a query includes parameters (e.g., a date range parameter), its results may change based on user input or the default value set in the query editor.
However, Databricks SQL Alerts will always use the default value set for the parameter at the time the alert is created. This means the alert doesn’t dynamically adapt to new date ranges and will not reflect changes unless the query is manually updated.
As a result, if the business logic behind the alert depends on changing date ranges or any user input, the alert will not trigger correctly, or may never trigger at all.
Therefore, the correct explanation contradicts Option B, which is incorrect in saying that alerts
cannot work with date-based queries at all. In fact, they can―as long as the query is static (i.e.,
without parameters).
Reference: Databricks SQL Alerts Documentation
Databricks Knowledge: “You cannot use alerts with queries that contain parameters.”
D
Explanation:
The reason the alert is not functioning as expected is because Databricks SQL Alerts do not support query parameters. This limitation applies to all types of parameters, including date parameters.
Here’s why:
Alerts require static, deterministic query results so they can compare values consistently during scheduled executions.
When a query includes parameters (e.g., a date range parameter), its results may change based on user input or the default value set in the query editor.
However, Databricks SQL Alerts will always use the default value set for the parameter at the time the alert is created. This means the alert doesn’t dynamically adapt to new date ranges and will not reflect changes unless the query is manually updated.
As a result, if the business logic behind the alert depends on changing date ranges or any user input, the alert will not trigger correctly, or may never trigger at all.
Therefore, the correct explanation contradicts Option B, which is incorrect in saying that alerts
cannot work with date-based queries at all. In fact, they can―as long as the query is static (i.e.,
without parameters).
Reference: Databricks SQL Alerts Documentation
Databricks Knowledge: “You cannot use alerts with queries that contain parameters.”
Question #8
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 #9
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 #10
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/
- A . By granting the stakeholders’ email addresses permissions to the dashboard
- B . By adding the stakeholders’ email addresses to the refresh schedule subscribers list
- C . By granting the stakeholders’ email addresses to the SQL Warehouse (formerly known as endpoint) subscribers list
- D . By downloading the dashboard as a PDF and emailing it to the stakeholders each time it is refreshed
Correct Answer: B
B
Explanation:
To share a Databricks SQL dashboard with stakeholders who do not have accounts in the Databricks deployment and ensure they are notified upon each refresh, the data analyst can add the stakeholders’ email addresses to the dashboard’s refresh schedule subscribers list. This approach allows the stakeholders to receive email notifications containing the latest dashboard updates
without requiring them to have direct access to the Databricks workspace. This method is efficient and minimizes effort, as it automates the notification process and ensures stakeholders remain informed of the most recent data insights.
Reference: Manage scheduled dashboard updates and subscriptions
B
Explanation:
To share a Databricks SQL dashboard with stakeholders who do not have accounts in the Databricks deployment and ensure they are notified upon each refresh, the data analyst can add the stakeholders’ email addresses to the dashboard’s refresh schedule subscribers list. This approach allows the stakeholders to receive email notifications containing the latest dashboard updates
without requiring them to have direct access to the Databricks workspace. This method is efficient and minimizes effort, as it automates the notification process and ensures stakeholders remain informed of the most recent data insights.
Reference: Manage scheduled dashboard updates and subscriptions
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