You can see it againUnder pressure people bring back shapes and positions long after the wording has gone.
Picture superiority · Shepard 1967, Standing 1973
DATABRICKSDatabricks Certified Data Engineer Professional
The Databricks Certified Data Engineer Professional certification validates your advanced skills in building, optimizing, and maintaining production-grade data engineering solutions on the Databricks Data Intelligence Platform. It is designed for experienced data engineers who architect secure, reliable, and cost-effective ETL pipelines, implement streaming workloads, and orchestrate workflows using Python and SQL. Earning this credential demonstrates your ability to deliver complex data engineering projects at scale.
Exam formatMultiple choice
Duration120 minutes
DeliveryDatabricks
Free questions639
Content last reviewed 30 July 2026 · Up to date
The certification
What Databricks Certified Data Engineer Professional proves, and what it asks of you
What this certification covers, who it is written for, and what the exam itself looks like on the day.
10domains
26objectives
199concepts
US $200exam fee
What it is
What this certification is
What it validates, who it is written for, and the experience it assumes.
About this certification
The Databricks Certified Data Engineer Professional certification validates your advanced skills in building, optimizing, and maintaining production-grade data engineering solutions on the Databricks Data Intelligence Platform, a multi-cloud environment. Successful candidates demonstrate expertise across core platform features such as Delta Lake, Unity Catalog, Auto Loader, Apache Spark™ Declarative Pipelines, Databricks Compute (including serverless), Lakeflow Jobs, and the Medallion Architecture.
This certification assesses your ability to design secure, reliable, and cost-effective ETL pipelines, process complex data from diverse sources using Python and SQL, and apply best practices in schema management, observability, governance, and performance optimization. You will also be tested on implementing streaming workloads, orchestrating workflows, leveraging DevOps and CI/CD, and deploying with tools like the Databricks CLI, REST API, and Asset Bundles. Individuals who pass this certification exam can be expected to complete advanced data engineering tasks using Databricks and its associated tools.
Who it’s for
This certification is for experienced data engineers who design, build, and maintain production-grade data pipelines on the Databricks platform. It is ideal for professionals who work with complex data processing, streaming workloads, and large-scale ETL solutions. You should be comfortable writing Python and SQL, implementing data governance and security, and using Databricks tools like the CLI, REST API, and Asset Bundles. The exam targets those who can apply best practices in schema management, observability, and performance optimization to deliver reliable and cost-effective data solutions.
Recommended experience
Hands-on experience performing the data engineering tasks outlined in the exam guide. Building and optimizing production-grade ETL pipelines; Implementing streaming workloads and workflow orchestration; Applying data governance and security best practices; Using Databricks CLI, REST API, and Asset Bundles for deployment
The syllabus
What you’ll learn
Every domain and objective Databricks measures, with the weight they carry on the exam.
The official Databricks exam outline · checked 30 July 2026 · See the source
Developing Code for Data Processing using Python and SQL
- Using Python and Tools for development
- Building and Testing an ETL pipeline with Lakeflow Spark Declarative Pipelines, SQL, and Apache Spark on the Databricks Platform
Data Ingestion & Acquisition
- Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.
- Create an append-only data pipeline capable of handling both batch and streaming data using Delta.
Data Transformation, Cleansing, and Quality
- Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets.
- Develop a quarantining process for bad data with Lakeflow Spark Declarative Pipelines, or autoloader in classic jobs.
Data Sharing and Federation
- Demonstrate delta sharing securely between Databricks deployments using Databricks to Databricks Sharing (D2D) or to external platforms using the open sharing protocol (D2O).
- Configure Lakehouse Federation with proper governance across the supported source Systems.
- Use Delta Share to share live data from Lakehouse to any computing platform.
Monitoring and Alerting
- Monitoring
- Alerting
Cost & Performance Optimization
- Understand how / why using Unity Catalog managed tables reduces operations overhead and maintenance burden.
- Understand delta optimization techniques, such as deletion vectors and liquid clustering.
- Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc.).
- Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency.
- Use the query profile to analyze the query and identify bottlenecks, such as bad data skipping, inefficient types of joins, and data shuffling.
Ensuring Data Security and Compliance
- Applying Data Security mechanisms.
- Ensuring Compliance
Data Governance
- Create and add descriptions/metadata about enterprise data to make it more discoverable.
- Demonstrate understanding of Unity Catalog permission inheritance model.
Debugging and Deploying
- Debugging and Troubleshooting
- Deploying CI/CD
Data Modeling
- Design and implement scalable data models using Delta Lake to manage large datasets.
- Simplify data layout decisions and optimize query performance using Liquid Clustering.
- Identify the benefits of using liquid Clustering over Partitioning and ZOrder.
- Design Dimensional Models for analytical workloads, ensuring efficient querying and aggregation.
On the day
The exam itself
Everything Databricks publishes about sitting it, and nothing we inferred.
Prerequisites
No mandatory prerequisites — this certification has no required predecessor exam or credential.
CertificationDatabricks Certified Data Engineer Professional
Exam formatMultiple choice
Duration120 minutes
Questions59 scored questions
DeliveryDatabricks
LanguagesEnglish, 日本語, Português BR, 한국어
PricingUS $200
Certification levelProfessional
After you pass
Where this credential goes next
The path Databricks lays out, how the credential is kept, and where to book.
Step-by-step path to Databricks Certified Data Engineer Professional
Credential earnedDatabricks Certified Data Engineer Professional Professional level certification Renewal and maintenance
Certifications need to be renewed by retaking the exam. Recertification is required every two years to maintain your certified status. To recertify, you must take the current version of the exam. Stay current with the latest technologies and maintain your certification.
Learn more about renewal requirements Lifecycle status
This certification is currently active and available. Databricks maintains this certification to validate current skills and industry relevance.
Exam status: ActiveMaintained by Databricks
Exam registration
Register for the exam through Databricks, Databricks’s authorized testing partner.
Schedule your examVisit the official Databricks certification page for exam policies and requirements.
View the official pageYour coach
And when you are serious, your coach Pip takes over
Your coach in the app reads what you have answered with the book closed and tells you one thing to do tonight. It will not count an answer you gave with the page open, and it will tell you when you are not ready.
See how the coach worksBefore you book
Questions people ask
How does the Databricks Certified Data Engineer Professional exam relate to the Associate-level Data Engineer certification?
The Professional certification is a higher-level credential that builds on the skills validated by the Databricks Certified Data Engineer Associate. While the Associate exam covers foundational data engineering tasks, the Professional exam focuses on advanced skills such as building production-grade pipelines, implementing streaming workloads, and optimizing cost and performance. There is no prerequisite to hold the Associate certification before taking the Professional exam.
Is the Databricks Certified Data Engineer Professional exam hands-on or lab-based?
The exam is a proctored, multiple-choice assessment. It does not include a hands-on lab component. However, the exam tests practical knowledge through scenario-based questions that require you to apply your experience with Databricks tools and best practices.
What is the retake policy for the Databricks Certified Data Engineer Professional exam?
Databricks does not publish a specific retake policy for this exam. For detailed information on retake policies and waiting periods, refer to the official Databricks Certification FAQ or contact Databricks support.
Can I recertify by passing a different Databricks exam?
No. To recertify for the Databricks Certified Data Engineer Professional, you must retake the current version of the Data Engineer Professional exam. Passing a different exam does not renew this certification.
What job roles does the Databricks Certified Data Engineer Professional credential map to?
This certification is designed for data engineers who are responsible for designing, building, and maintaining production-grade data pipelines and ETL solutions on the Databricks platform. It is also relevant for data architects and data platform engineers who work with large-scale data processing and streaming workloads.
Are there any regional differences in the delivery of the Databricks Certified Data Engineer Professional exam?
The exam is available online and at test centers. Databricks does not publish regional differences in delivery. For specific availability in your region, please check the exam delivery platform during registration.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 639 questions, free, no account needed.
The curriculum
The whole exam, mapped and written
Every domain, every objective, and every concept Databricks measures — each one written out.
10domains
26objectives
199concepts
134free pages
The domains, and what sits inside them
Every objective below is a page you can open and practise now, without an account.
The official Databricks exam outline · checked 30 July 2026 · See the source
1Developing Code for Data Processing using Python and SQL52 questions
2Data Ingestion & Acquisition58 questions
Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.15 concepts · 7 pagesOpen →Create an append-only data pipeline capable of handling both batch and streaming data using Delta.8 concepts · 5 pagesOpen →
3Data Transformation, Cleansing, and Quality39 questions
Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets.6 concepts · 4 pagesOpen →Develop a quarantining process for bad data with Lakeflow Spark Declarative Pipelines, or autoloader in classic jobs.5 concepts · 4 pagesOpen →
4Data Sharing and Federation77 questions
Demonstrate delta sharing securely between Databricks deployments using Databricks to Databricks Sharing (D2D) or to external platforms using the open sharing protocol (D2O).7 concepts · 5 pagesOpen →Configure Lakehouse Federation with proper governance across the supported source Systems.9 concepts · 5 pagesOpen →Use Delta Share to share live data from Lakehouse to any computing platform.7 concepts · 6 pagesOpen →
5Monitoring and Alerting43 questions
6Cost & Performance Optimization116 questions
Understand how / why using Unity Catalog managed tables reduces operations overhead and maintenance burden.8 concepts · 5 pagesOpen →Understand delta optimization techniques, such as deletion vectors and liquid clustering.9 concepts · 6 pagesOpen →Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc.).8 concepts · 6 pagesOpen →Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency.5 concepts · 3 pagesOpen →Use the query profile to analyze the query and identify bottlenecks, such as bad data skipping, inefficient types of joins, and data shuffling.5 concepts · 4 pagesOpen →
7Ensuring Data Security and Compliance51 questions
8Data Governance48 questions
9Debugging and Deploying59 questions
10Data Modeling96 questions
Design and implement scalable data models using Delta Lake to manage large datasets.7 concepts · 6 pagesOpen →Simplify data layout decisions and optimize query performance using Liquid Clustering.7 concepts · 4 pagesOpen →Identify the benefits of using liquid Clustering over Partitioning and ZOrder.6 concepts · 5 pagesOpen →Design Dimensional Models for analytical workloads, ensuring efficient querying and aggregation.7 concepts · 5 pagesOpen →
Not a table of contents
Every line on this map already opens
In front of every objective the practice pages are already there, free and without an account. This is one objective, opened.
Using Python and Tools for development5 pagesOpen →
Free in front of it
23 questions on this objective, five to a page. Every range above is a real page, open now, with no account.
Reading the map is not the same as knowing the ground
The curriculum tells you what is on the exam. Proving you know it is a different job — and it is the one the closed-book run does.
Free questions
639 questions. No account, no card.
The whole bank is open. 5 questions to a page, every answer explained, and a discussion thread on each one.
639questions
134pages
26objectives
Freealways
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1Developing Code for Data Processing using Python and SQL52 questions
2Data Ingestion & Acquisition58 questions
3Data Transformation, Cleansing, and Quality39 questions
4Data Sharing and Federation77 questions
5Monitoring and Alerting43 questions
6Cost & Performance Optimization116 questions
7Ensuring Data Security and Compliance51 questions
8Data Governance48 questions
9Debugging and Deploying59 questions
10Data Modeling96 questions
What a practice page gives you
1Five to a page
Short enough to finish, long enough to matter.
2Every answer explained
Not only which one is right — why the others are wrong.
3A discussion on each
Ask, answer, and vote. Every question has its own thread.
A question, and the idea behind it
Every question comes from a concept
These are not trivia. Each one is written against a concept in the book, so when you get one wrong there is somewhere to go and find out why.
The visual book
See it once. Carry it into the exam.
The pages shown here come from our AI-900 book — an example of how each concept is written in plain language and, where the idea needs one, drawn as a full page you can take in at a glance.
199concepts
639free questions
Why a picture, and not another paragraph
Three reasons, and each one is a real finding rather than a slogan.
You can see it againThe whole idea at onceWhere it starts, what happens in the middle, what comes out, and the mistake to avoid.
Multimedia principle · MayerThe look-alikes sit togetherThe pairs the exam tests are drawn side by side, so the difference is seen, not told.
Dual coding · PaivioWhat one page is made of
You are never asked to read a poster here — only to see how one is built. After that, every other page is legible at a glance.
1A numbered strip on top
The idea as a sequence, followed with a finger before a word is read.
2Panels for the parts
What it is, how the machine learns it, when it is the right tool.
3The trap, framed
The distinction the exam tests, given its own box instead of buried in prose.
4One line at the bottom
The sentence to carry into the exam room.
Open book, on purpose
Answer it with the picture beside you
This is the part that teaches. The illustration and the written explanation stay where they are while you work, so a scenario stops being a memory test and becomes something you can simply look at.
It teaches · it does not count
A smartphone uses AI to unlock when the owner looks at the camera. Which AI capability is being used?
AImage classification
BFacial recognition
COptical character recognition
DObject detection
Look at the five steps. Detect finds a face, align straightens it, encode turns it into numbers, search compares it with a stored face, and decide answers one question: is this the same person? Classification would only say “a face”. Detection would only draw a box. Neither decides whose.
Then the page shuts, and it starts counting
The same questions come back with the book closed — that run is the one that counts. After it, your coach picks one thing for tonight, sized to the time you have, and brings pages back before you lose them.
Testing effect · Roediger & Karpicke 2006 · spacing effect · Cepeda et al. 2006
Official resources
Straight from Databricks
Where the exam is defined, scheduled and scored.
3official links
199concepts mapped
The source
Databricks’s own pages
We link to them rather than repeat them, so nothing here goes stale behind them.
Checked against the source
Every objective on the official Databricks outline already has questions here
We build from the official skills outline, not from a summary of it — 26 objectives, 199 concepts written under them, and free questions against every one. When Databricks changes the outline, this page changes with it.
26objectives on the outline→199concepts written→639free questions
An official page tells you what will be asked. It cannot tell you what you will get right.
That is the only question worth answering the night before, and no link answers it. You answer it by taking the questions with the book closed, and seeing what comes back.