Databricks-Certified-Data-Analyst-Associate Free Practice Test

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?

1. A. SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary
2. B. SELECT price FROM customer_summary
3. C. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
4. D. SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
5. E. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary

Correct Answer: E
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. References:
✑ What are user-defined functions (UDFs)?
✑ User-defined scalar functions - SQL V

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?

1. A. Reduce the SQL endpoint cluster size
2. B. Increase the SQL endpoint cluster size
3. C. Turn off the Auto stop feature
4. D. Increase the minimum scaling value
5. E. Use a Serverless SQL endpoint

Correct Answer: E
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. References: Databricks SQL endpoints, Serverless SQL endpoints, SQL endpoint clusters

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?

1. A. Separate endpoints for each section
2. B. Separate queries for each section
3. C. Markdown-based text boxes
4. D. Direct text written into the dashboard in editing mode
5. E. Separate color palettes for each section

Correct Answer: C
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. References: Dashboards in notebooks, How to add text to a dashboard in Databricks

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?

1. A. As an exact substitute with the same level of functionality
2. B. As a substitute with less functionality
3. C. As a complete replacement with additional functionality
4. D. As a complementary tool for professional-grade presentations
5. E. As a complementary tool for quick in-platform Bl work

Correct Answer: 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. References: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

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?

1. A. Data blending
2. B. Last-mile
3. C. Data testing
4. D. Last-mile ETL
5. E. Data enhancement

Correct Answer: D
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, dataenrichment, 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. References: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL