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 Associate
The Databricks Certified Data Engineer Associate certification validates your ability to use the Databricks Data Intelligence Platform for foundational data engineering tasks. It covers data ingestion, ETL with PySpark, Lakeflow Jobs, CI/CD, and governance. Ideal for data engineers building and maintaining batch and streaming pipelines, this credential demonstrates you can turn raw data into reliable, governed analytics-ready datasets.
Exam formatMultiple choice
Duration90 minutes
DeliveryDatabricks
Free questions623
Content last reviewed 30 July 2026 · Up to date
The certification
What Databricks Certified Data Engineer Associate 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.
7domains
33objectives
207concepts
$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 Associate certification assesses your ability to use the Databricks Data Intelligence Platform to complete introductory data engineering tasks. The exam validates your knowledge of the platform's workspace, architecture, and capabilities, along with core tasks such as data ingestion, loading, transformation, and modeling. You'll demonstrate hands-on skills in performing ETL with PySpark, working with Lakeflow Jobs, and implementing CI/CD practices.
Beyond the mechanics, the certification tests your understanding of troubleshooting, monitoring, and optimization techniques, as well as governance and security within the Databricks Platform. Earning this credential signals that you can build and maintain reliable data pipelines that power analytics and AI initiatives, making you a more effective contributor to modern data teams.
Who it’s for
This certification is designed for data engineers who build, maintain, and optimize batch and streaming data pipelines on the Databricks Platform. It's ideal for practitioners who are comfortable with SQL and Python and want to formalize their skills in data ingestion, transformation, and orchestration. If you're early in your data engineering career or looking to validate your foundational Databricks skills, this credential provides a clear benchmark for your abilities and helps you stand out in a competitive job market.
Recommended experience
Hands-on experience performing the data engineering tasks outlined in the exam guide, typically 6+ months of practical work with the Databricks Platform. Performing ETL tasks using PySpark and SQL; Working with Lakeflow Jobs to orchestrate and schedule pipelines; Implementing CI/CD for data pipelines; Applying governance and security best practices within Databricks
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
Databricks Intelligence Platform
- Understand the core components of the Databricks Data Intelligence Platform, such as its architecture, Delta Lake, and Unity Catalog.
- Understand Databricks Data Intelligence Platform’s compute services, including their characteristics, limitations, and cost models, and select the most suitable option for each workload use case.
Data Ingestion and Loading
- Enable and detail data ingestion patterns, including batch, streaming, and incremental loading, and import data from sources such as local files, Lakeflow Connect standard connectors, and Lakeflow Connect managed connectors.
- Use the COPY INTO command to incrementally load files from cloud object storage (ADLS/S3/GCS) into Unity‑Catalog–governed tables.
- Use Auto Loader with schema enforcement and schema evolution in batch modes (for example, directory listing or file notification) to land data into UnityCatalog–governed tables.
- Configure Lakeflow Connect to reliably ingest data from diverse enterprise sources into UnityCatalog–governed tables.
- Use JDBC/ODBC or REST clients in notebooks to land data into cloud storage or directly into UnityCatalog–governed tables, usually orchestrated and scheduled with Lakeflow Jobs.
- Prioritize between Auto Loader, Lakeflow Connect (standard and managed connectors), partner connectors, and other ingestion methods based on technical requirements such as data volume, ingestion frequency, data types, and governance needs with Unity Catalog.
- Ingest semi-structured and unstructured data (for example, JSON and nested data) via Lakeflow Connect and other managed connectors into UnityCatalog–governed Delta tables.
Data Transformation and Modeling
- Implement data cleaning by reading bronze tables with PySpark/SQL, cleaning nulls, standardizing data types, and writing to new silver tables.
- Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, and union all.
- Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays.
- Perform data deduplication operations and aggregate operations on DataFrames, such as count, approximate count distinct, and mean, summary.
- Understand the basic tuning parameters (spark.sql.shuffle.partitions:, spark.default.parallelism, spark.executor/driver.memory, spark.sql.autoBroadcastJoinThreshold) and re-measure the performance.
- Understand the difference between, and how to build, Gold layer objects such as materialized views, views, streaming tables, and tables for BI and analytics teams in Unity Catalog.
- Apply data quality checks and validation rules to ensure reliable Silver and Gold datasets.
Working with Lakeflow Jobs
- Implement control flows (retries and conditional tasks such as branching and looping) using Lakeflow Jobs for pipeline orchestration
- Configure common tasks (notebook, SQL query, dashboard, and pipeline tasks) and their dependencies using Lakeflow Jobs and its DAG‑based task graph
- Implement job schedules using Lakeflow Jobs with an understanding of trigger types (scheduled, file arrival, and table update)
- Choose between time‑based and data‑driven triggers based on data availability and pipeline dependencies.
Implementing CI/CD
- Manage your code development workflow within the Databricks workspace UI, including creating and switching between branches in Databricks Git Folders (formerly Databricks Repos), committing and pushing changes, and creating pull requests using Databricks Git integration.
- Understand environment-specific configuration using Automation Bundle (formerly Databricks Asset Bundles) variables and overrides while promoting the same codebase across dev, test, and prod targets.
- Deploy Declarative Automation Bundles (formerly Databricks Asset Bundles) to package, configure, and promote Lakeflow Jobs, Lakeflow Spark Declarative Pipelines, and other workspace assets across dev, test, and prod environments.
- Understand the Databricks CLI to validate, deploy, and manage Declarative Automation Bundles (formerly Databricks Asset Bundles) and other workspace assets in automated CI/CD workflows.
Troubleshooting, Monitoring, and Optimization
- Identify trends in job performance using the Lakeflow Jobs run history view to compare current execution times against historical baselines.
- Use the Lakeflow Jobs UI to monitor pipeline health by interpreting job statuses, viewing DAG‑based task graphs to spot upstream blockers, and tracking pipeline run times and failure rates.
- Identify common performance bottlenecks such as data skew, shuffling, and disk spilling by interpreting stage-level metrics in the Spark UI.
- Understand the features of Liquid Clustering and predictive optimization.
- Diagnose cluster startup failures, library conflicts, and out-of-memory issues.
Governance and Security
- Differentiate between managed and external tables in Unity Catalog and perform basic operations (create, modify, delete, and convert between managed and external tables) on them.
- Configure access controls using the UI and SQL by applying GRANT, REVOKE, and DENY privileges to principals (users, groups, and service principals) at appropriate levels of the security hierarchy.
- Understand column-level masking and row-level security to restrict data visibility based on user groups.
- Understand Unity Catalog ABAC policies to centrally control row-level filtering and column masking for sensitive data.
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 Associate
Exam formatMultiple choice
Duration90 minutes
Questions45 scored questions
DeliveryDatabricks
LanguagesEnglish, 日本語, Português BR, 한국어
Pricing$200
Certification levelAssociate
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 Associate
Credential earnedDatabricks Certified Data Engineer Associate Associate level certification Renewal and maintenance
The Databricks Certified Data Engineer Associate certification is valid for two years. To maintain your certified status, you must recertify by taking the current version of the exam before the validity period expires. 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
Is the Databricks Certified Data Engineer Associate exam available in my region?
The exam is delivered online or at test centers, with availability varying by region. Online proctoring offers broader access, while test center availability depends on your location.
What are the technical requirements for taking the online proctored exam?
You must review the technical requirements for online proctored exams and run a system check before registering. This ensures your computer and environment meet the necessary specifications for a smooth testing experience.
Can I reschedule or cancel my exam appointment?
Rescheduling and cancellation policies are managed through Databricks' exam delivery platform. You should review the platform's terms and conditions for specific deadlines and any associated fees.
What should I do if I experience technical issues during the exam?
If you encounter technical difficulties during the exam, Databricks' support team is available to help. You should file a ticket with their Help Center to get assistance and resolve the issue promptly.
Are there any test aides allowed during the exam?
No test aides are allowed during the exam. The exam is closed-book, and you must rely solely on your knowledge and skills.
How soon will I receive my exam results?
Databricks does not specify an exact timeline for score release. Typically, results are available shortly after completing the exam, but you should check the exam delivery platform for the most accurate information.
What job roles does this certification align with?
This certification is designed for data engineers who build and maintain data pipelines on the Databricks Platform. It validates skills relevant to roles such as Data Engineer, ETL Developer, and Data Platform Engineer.
Can I recertify by passing a higher-level Databricks exam?
Recertification requires taking the current version of the Data Engineer Associate exam. Passing a higher-level exam, such as the Data Engineer Professional, does not automatically renew this certification.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 623 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.
7domains
33objectives
207concepts
137free 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
1Databricks Intelligence Platform39 questions
Understand the core components of the Databricks Data Intelligence Platform, such as its architecture, Delta Lake, and Unity Catalog.5 concepts · 5 pagesOpen →Understand Databricks Data Intelligence Platform’s compute services, including their characteristics, limitations, and cost models, and select the most suitable option for each workload use case.5 concepts · 3 pagesOpen →
2Data Ingestion and Loading191 questions
Enable and detail data ingestion patterns, including batch, streaming, and incremental loading, and import data from sources such as local files, Lakeflow Connect standard connectors, and Lakeflow Connect managed connectors.6 concepts · 5 pagesOpen →Use the COPY INTO command to incrementally load files from cloud object storage (ADLS/S3/GCS) into Unity‑Catalog–governed tables.7 concepts · 4 pagesOpen →Use Auto Loader with schema enforcement and schema evolution in batch modes (for example, directory listing or file notification) to land data into UnityCatalog–governed tables.11 concepts · 7 pagesOpen →Configure Lakeflow Connect to reliably ingest data from diverse enterprise sources into UnityCatalog–governed tables.10 concepts · 8 pagesOpen →Use JDBC/ODBC or REST clients in notebooks to land data into cloud storage or directly into UnityCatalog–governed tables, usually orchestrated and scheduled with Lakeflow Jobs.8 concepts · 5 pagesOpen →Prioritize between Auto Loader, Lakeflow Connect (standard and managed connectors), partner connectors, and other ingestion methods based on technical requirements such as data volume, ingestion frequency, data types, and governance needs with Unity Catalog.9 concepts · 6 pagesOpen →Ingest semi-structured and unstructured data (for example, JSON and nested data) via Lakeflow Connect and other managed connectors into UnityCatalog–governed Delta tables.7 concepts · 6 pagesOpen →
3Data Transformation and Modeling102 questions
Implement data cleaning by reading bronze tables with PySpark/SQL, cleaning nulls, standardizing data types, and writing to new silver tables.4 concepts · 3 pagesOpen →Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, and union all.7 concepts · 3 pagesOpen →Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays.8 concepts · 3 pagesOpen →Perform data deduplication operations and aggregate operations on DataFrames, such as count, approximate count distinct, and mean, summary.5 concepts · 2 pagesOpen →Understand the basic tuning parameters (spark.sql.shuffle.partitions:, spark.default.parallelism, spark.executor/driver.memory, spark.sql.autoBroadcastJoinThreshold) and re-measure the performance.5 concepts · 4 pagesOpen →Understand the difference between, and how to build, Gold layer objects such as materialized views, views, streaming tables, and tables for BI and analytics teams in Unity Catalog.7 concepts · 5 pagesOpen →Apply data quality checks and validation rules to ensure reliable Silver and Gold datasets.5 concepts · 3 pagesOpen →
4Working with Lakeflow Jobs49 questions
Implement control flows (retries and conditional tasks such as branching and looping) using Lakeflow Jobs for pipeline orchestration5 concepts · 3 pagesOpen →Configure common tasks (notebook, SQL query, dashboard, and pipeline tasks) and their dependencies using Lakeflow Jobs and its DAG‑based task graph6 concepts · 3 pagesOpen →Implement job schedules using Lakeflow Jobs with an understanding of trigger types (scheduled, file arrival, and table update)4 concepts · 3 pagesOpen →Choose between time‑based and data‑driven triggers based on data availability and pipeline dependencies.7 concepts · 3 pagesOpen →
5Implementing CI/CD89 questions
Manage your code development workflow within the Databricks workspace UI, including creating and switching between branches in Databricks Git Folders (formerly Databricks Repos), committing and pushing changes, and creating pull requests using Databricks Git integration.6 concepts · 5 pagesOpen →Understand environment-specific configuration using Automation Bundle (formerly Databricks Asset Bundles) variables and overrides while promoting the same codebase across dev, test, and prod targets.5 concepts · 5 pagesOpen →Deploy Declarative Automation Bundles (formerly Databricks Asset Bundles) to package, configure, and promote Lakeflow Jobs, Lakeflow Spark Declarative Pipelines, and other workspace assets across dev, test, and prod environments.5 concepts · 4 pagesOpen →Understand the Databricks CLI to validate, deploy, and manage Declarative Automation Bundles (formerly Databricks Asset Bundles) and other workspace assets in automated CI/CD workflows.7 concepts · 5 pagesOpen →
6Troubleshooting, Monitoring, and Optimization88 questions
Identify trends in job performance using the Lakeflow Jobs run history view to compare current execution times against historical baselines.4 concepts · 5 pagesOpen →Use the Lakeflow Jobs UI to monitor pipeline health by interpreting job statuses, viewing DAG‑based task graphs to spot upstream blockers, and tracking pipeline run times and failure rates.5 concepts · 4 pagesOpen →Identify common performance bottlenecks such as data skew, shuffling, and disk spilling by interpreting stage-level metrics in the Spark UI.4 concepts · 5 pagesOpen →Understand the features of Liquid Clustering and predictive optimization.7 concepts · 4 pagesOpen →Diagnose cluster startup failures, library conflicts, and out-of-memory issues.3 concepts · 2 pagesOpen →
7Governance and Security65 questions
Differentiate between managed and external tables in Unity Catalog and perform basic operations (create, modify, delete, and convert between managed and external tables) on them.5 concepts · 2 pagesOpen →Configure access controls using the UI and SQL by applying GRANT, REVOKE, and DENY privileges to principals (users, groups, and service principals) at appropriate levels of the security hierarchy.8 concepts · 5 pagesOpen →Understand column-level masking and row-level security to restrict data visibility based on user groups.10 concepts · 4 pagesOpen →Understand Unity Catalog ABAC policies to centrally control row-level filtering and column masking for sensitive data.7 concepts · 3 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.
Understand the core components of the Databricks Data Intelligence Platform, such as its architecture, Delta Lake, and Unity Catalog.5 pagesOpen →
Free in front of it
24 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
623 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.
623questions
137pages
33objectives
Freealways
Start anywhereUnderstand the core components of the Databricks Data Intelligence Platform, such as its architecture, Delta Lake, and Unity Catalog. 24 questions · 5 pages
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1Databricks Intelligence Platform39 questions
2Data Ingestion and Loading191 questions
3Data Transformation and Modeling102 questions
4Working with Lakeflow Jobs49 questions
5Implementing CI/CD89 questions
6Troubleshooting, Monitoring, and Optimization88 questions
7Governance and Security65 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.
207concepts
623free 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.
2official links
207concepts 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 — 33 objectives, 207 concepts written under them, and free questions against every one. When Databricks changes the outline, this page changes with it.
33objectives on the outline→207concepts written→623free 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.