Free Databricks-Certified-Data-Analyst-Associate: Databricks Certified Data Analyst Associate Exam Questions and Answers
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Last updated: September 19, 2026
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Question #1
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?
Please select an optionIncorrectCorrect 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
Was this answer correct?Question #2
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?
Please select an optionIncorrectCorrect answer: B
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. References: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL
Was this answer correct?Question #3
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?
Please select an optionIncorrectCorrect answer: D
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. References: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran
Was this answer correct?Question #4
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run: After runningSELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks. After logging back in two days later, what is the status of thestakeholders.eur_customersview?
Please select an optionIncorrectCorrect answer: B
The command you sent creates a TEMP VIEW, which is a type of view that is only visible and accessible to the session that created it. When the session ends or the user logs out, the TEMP VIEW is automatically dropped and cannot be queried anymore. Therefore, after logging back in two days later, the status of the stakeholders.eur_customers view is that it has been dropped and SELECT * FROM stakeholders.eur_customers will result in an error. The other options are not correct because: • A. The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out. • C. The view is not available in the metastore, as it is a TEMP VIEW that is not registered in the metastore. The underlying data cannot be accessed with SELECT * FROM delta. stakeholders.eur_customers, as this is not a valid syntax for querying a Delta Lake table. The correct syntax would be SELECT * FROM delta.dbfs:/stakeholders/eur_customers, where the location path is enclosed in backticks. However, this would also result in an error, as the TEMP VIEW does not write any data to the file system and the location path does not exist. • D. The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out. Data in views are not automatically deleted after logging out, as views do not store any data. They are only logical representations of queries on base tables or other views. • E. The view has not been converted into a table, as there is no automatic conversion between views and tables in Databricks. To create a table from a view, you need to use a CREATE TABLE AS statement or a similar command. References: CREATE VIEW | Databricks on AWS, Solved: How do temp views actually work? - Databricks - 20136, temp tables in Databricks - Databricks - 44012, Temporary View in Databricks - BIG DATA PROGRAMMERS, Solved: What is the difference between a Temporary View an ??
Was this answer correct?Question #5
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?
Please select an optionIncorrectCorrect answer: A
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. References: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows
Was this answer correct?Question #6
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?
Please select an optionIncorrectCorrect 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
Was this answer correct?Question #7
A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform. Which of the following projects should be completed in Databricks SQL?
Please select an optionIncorrectCorrect answer: C
Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency. References: • Databricks SQL overview • Databricks SQL use cases • Joining data sources
Was this answer correct?Question #8
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?
Please select an optionIncorrectCorrect 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
Was this answer correct?Question #9
In which of the following situations should a data analyst use higher-order functions?
Please select an optionIncorrectCorrect answer: C
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. References: 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
Was this answer correct?Question #10
How can a data analyst determine if query results were pulled from the cache?
Please select an optionIncorrectCorrect answer: 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. References: 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
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Other Databricks certifications
- Certified Machine Learning Associate (opens in a new tab)
- Certified Machine Learning Professional (opens in a new tab)
- Certified Data Engineer Associate (opens in a new tab)
- Certified Data Analyst Associate (opens in a new tab)
- Certified Associate Developer for Apache Spark (opens in a new tab)
- Certified Data Engineer Professional (opens in a new tab)
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FAQ
Learn More: https://www.databricks.com/learn/training/certification
- Q1: What are Databricks Certification Exams?
- A: Databricks Certification Exams validate your expertise in using Databricks’ unified data analytics platform. These certifications demonstrate your proficiency in data engineering, data analysis, and machine learning, utilizing Databricks tools and the Apache Spark framework.
- Q2: Why should I pursue Databricks Certification?
- A: Databricks Certification enhances your professional credibility, showcasing your skills and knowledge in big data analytics and machine learning. This can lead to better job opportunities, higher salaries, and career advancement in data science, data engineering, and IT industries.
- Q3: What are the benefits of Databricks Certification?
- A: Benefits include recognition as a certified data professional, improved job performance, access to exclusive resources, continuing education opportunities, and staying current with the latest data analytics technologies and best practices.
- Q4: Who should take Databricks Certification Exams?
- A: Data engineers, data scientists, data analysts, machine learning practitioners, and anyone involved in processing and analyzing large datasets should consider these certifications to validate their expertise and advance their careers.
- Q5: What types of Databricks Certification Exams are available?
- A: Databricks offers various certification paths, including:
- Q6: How do I prepare for Databricks Certification Exams?
- A: Preparation can include official Databricks training courses, study guides, practice exams, online tutorials, and hands-on experience with Databricks tools and the Apache Spark framework.
- Q7: Where can I take Databricks Certification Exams?
- A: Databricks Certification Exams can be taken online, providing flexibility to fit your schedule and location.
- Q8: How do Databricks Certifications impact my career?
- A: Databricks Certifications significantly boost your career by demonstrating your expertise to employers, making you a more competitive candidate for advanced roles and promotions in data science, data engineering, and IT.
- Q9: Are there any prerequisites for Databricks Certification Exams?
- A: Some exams may have prerequisites, such as foundational knowledge or prior experience with Databricks and Apache Spark. Check the specific requirements for each certification path on the Databricks website.
- Q10: How often do I need to recertify for Databricks Certifications?
- A: Databricks Certifications typically require recertification every two years to ensure that certified professionals stay updated with the latest data analytics technologies and industry practices.