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 Analyst Associate
The Databricks Certified Data Analyst Associate certification validates your ability to use the Databricks Data Intelligence Platform to perform introductory data analysis tasks. It is designed for data analysts who query, visualize, and manage data using Databricks SQL, Unity Catalog, and AI/BI Genie. Earning it demonstrates that you can turn raw data into trusted insights and share them through dashboards and visualizations.
Exam formatMultiple choice
Duration90 minutes
DeliveryDatabricks
Free questions817
Content last reviewed 30 July 2026 · Up to date
The certification
What Databricks Certified Data Analyst 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.
9domains
39objectives
245concepts
$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 Analyst Associate certification validates your proficiency with the Databricks Data Intelligence Platform for introductory data analysis tasks. The exam assesses your ability to manage data with Unity Catalog—including discovering, querying, cleaning, and managing certified datasets—and to import data using methods such as the UI, S3 ingestion, Delta Sharing, API-driven intake, Auto Loader, and the Marketplace. You will also be tested on executing and optimizing queries for data analysis, including creating views, performing aggregate operations, combining tables with joins, filtering, sorting, and analyzing queries using auditing, history, logs, and Liquid clustering.
Beyond querying, the exam covers the basics of working with dashboards and visualizations, understanding the fundamentals of developing, sharing, and maintaining AI/BI Genie spaces, data modeling with Databricks SQL, and securing data by adhering to best practices for data storage and management. The certification is intended for data analysts who use SQL to transform data into insights, and it validates the skills needed to build queries, visualizations, and dashboards that drive business decisions.
Who it’s for
This certification is for data analysts who use the Databricks Data Intelligence Platform to perform introductory data analysis tasks. It is designed for individuals who work with Databricks SQL to query, visualize, and manage data, and who need to turn raw data into actionable insights for their organizations. Ideal candidates are comfortable writing SQL queries, creating dashboards and visualizations, and managing data assets within a lakehouse environment. They typically have some hands-on experience with Databricks and are looking to validate their skills in data analysis, data management, and data visualization.
Recommended experience
6+ months of hands-on experience performing the data analysis tasks outlined in the exam guide. Managing data with Unity Catalog, including discovering, querying, cleaning, and managing certified datasets; Importing data using various methods such as the UI, S3 ingestion, Delta Sharing, API-driven intake, Auto Loader, and the Marketplace; Executing and optimizing queries using Databricks SQL and Databricks SQL Warehouses; Creating dashboards and visualizations in Databricks; Developing, sharing, and maintaining AI/BI Genie spaces; Data modeling with Databricks SQL; Securing data by adhering to best practices for data storage and management
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
Section 1: Understanding of Databricks Data Intelligence Platform
- Describe the core components of the Databricks Intelligence Platform, including Mosaic AI, DeltaLive tables, Lakeflow Jobs, Data Intelligence Engine, Delta Lake, Unity Catalog, and Databricks SQL.
- Understand catalogs, schemas, managed and external tables, access controls, views, certified tables, and lineage within the Catalog Explorer interface.
- Describe the role and features of Databricks Marketplace.
Section 2: Managing Data
- Use Unity Catalog to discover, query, and manage certified datasets.
- Use the Catalog Explorer to tag a data asset and view its lineage.
- Perform data cleaning on Unity Catalog Tables in SQL, including removing invalid data or handling missing values.
Section 3: Importing Data
- Explain the approaches for bringing data into Databricks, covering ingestion from S3, data sharing with external systems via Delta Sharing, API-driven data intake, the Auto Loader feature, and Marketplace.
- Use the Databricks Workspace UI to upload a data file to the platform.
Section 4: Executing queries using Databricks SQL and Databricks SQL Warehouses
- Utilize Databricks Assistant within a Notebook or SQL Editor to facilitate query writing and debugging.
- Explain the role a SQL Warehouse plays in query execution.
- Querying cross-system analytics by joining data from a Delta table and a federated data source.
- Create a materialized view, including knowing when to use Streaming Tables and Materialized Views, and differentiate between dynamic and materialized views.
- Perform aggregate operations such as count, approximate count distinct, mean, and summary statistics.
- Write queries to combine tables using various join operations (inner, left, right, and so on) with single or multiple keys, as well as set operations like union and union all, including the differences between the joins (inner, left, right, and so on).
- Perform sorting and filtering operations on a table.
- Create managed tables and external tables, including creating tables by joining data from multiple sources (e.g., CSV, Parquet, Delta tables) to create unified datasets, including Unity Catalog.
- Use Delta Lake's time travel to access and query historical data versions.
Section 5: Analyzing Queries
- Understand the Features, Benefits, and Supported Workloads of Photon.
- Identify poorly performing queries in the Databricks Intelligence platform, such as Query Insights, Query Profiler log, etc.
- Utilize Delta Lake to audit and view history, validate results, and compare historical results or trends.
- Utilize query history and caching to reduce development time and query latency
- Apply Liquid Clustering to improve query speed when filtering large tables on specific columns.
- Fix a query to achieve the desired results.
Section 6: Working with Dashboards and Visualizations in Databricks
- Build dashboards using AI/BI Dashboards, including multi-tabs/page layouts, multiple data sources/datasets, and widgets (visualizations, text, images).
- Create visualizations in notebooks and the SQL editor.
- Work with parameters in SQL queries and dashboards, including defining, configuring, and testing parameters.
- Configure permissions through the UI to share dashboards with workspace users/groups, external users through shareable links, and embed dashboards in external apps.
- Schedule an automatic dashboard refresh.
- Configure an alert with a desired threshold and destination.
- Identify the effective visualization type to communicate insights clearly.
Section 7: Developing, Sharing, and Maintaining AI/BI Genie spaces
- Describe the purpose, key features, and components of AI/BI Genie spaces.
- Create Genie spaces by defining reasonable sample questions and domain-specific instructions, choosing SQL warehouses, curating Unity Catalog datasets (tables, views...), and vetting queries as Trusted Assets.
- Assign permissions via the UI and distribute Genie spaces using embedded links and external app integrations.
- Optimize AI/BI Genie spaces by tracking user questions, response accuracy, and feedback; updating instructions and trusted assets based on stakeholder input; validating accuracy with benchmarks; refreshing Unity Catalog metadata.
Section 8: Data Modeling with Databricks SQL
- Apply industry-standard data modeling techniques, such as star, snowflake, and data vault schemas, to analytical workloads.
- Understand how industry-standard models align with the Medallion Architecture.
Section 9: Securing Data
- Use Unity Catalog roles and sharing settings to ensure workspace objects are secure.
- Understand how the 3-level namespace(Catalog / Schema / Tables or Volumes) works in the Unity Catalog.
- Apply best practices for storage and management to ensure data security, including table ownership and PII protection.
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 Analyst Associate
Exam formatMultiple choice
Duration90 minutes
Questions45 questions
DeliveryDatabricks
LanguagesEnglish
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 Analyst Associate
Credential earnedDatabricks Certified Data Analyst Associate Associate level certification Renewal and maintenance
The Databricks Certified Data Analyst Associate certification is valid for two years. 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 Analyst Associate relate to other Databricks certifications?
The Data Analyst Associate is part of Databricks' role-based certification program. It is designed for data analysts, while other certifications target data engineers, machine learning engineers, and generative AI engineers. There is no higher-level Data Analyst certification currently offered.
Do I need to earn a lower-level Databricks certification before taking this exam?
No. Databricks lists no mandatory prerequisites for this exam. However, related training is highly recommended, and 6+ months of hands-on experience is recommended.
Is the exam available in test centers or only online?
The exam is available both online proctored and at test centers. You can choose the delivery method that works best for you during registration.
What are the technical requirements for taking the online proctored exam?
You should review the technical requirements for taking an online proctored exam and run a system check before your exam. Databricks provides guidance on the exam page to help you prepare.
Are there any unscored items on the exam?
Yes. Exams may include unscored items to gather statistical information for future use. These items are not identified on the form and do not impact your score. Additional time is factored into the exam to account for this content.
What job roles does the Databricks Certified Data Analyst Associate credential map to?
This certification is designed for data analysts who transform data into insights by creating queries, data visualizations, and dashboards using Databricks SQL and its capabilities.
Can I recertify by passing a different Databricks exam?
No. To recertify, you must take the current version of the Databricks Certified Data Analyst Associate exam. Recertification is required every two years to maintain your certified status.
Is there a hands-on or lab component in the exam?
The exam is a proctored certification with multiple choice questions. There is no hands-on lab component mentioned in the official exam details.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 817 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.
9domains
39objectives
245concepts
180free 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
1Section 1: Understanding of Databricks Data Intelligence Platform95 questions
Describe the core components of the Databricks Intelligence Platform, including Mosaic AI, DeltaLive tables, Lakeflow Jobs, Data Intelligence Engine, Delta Lake, Unity Catalog, and Databricks SQL.8 concepts · 7 pagesOpen →Understand catalogs, schemas, managed and external tables, access controls, views, certified tables, and lineage within the Catalog Explorer interface.9 concepts · 6 pagesOpen →Describe the role and features of Databricks Marketplace.8 concepts · 7 pagesOpen →
2Section 2: Managing Data43 questions
Use Unity Catalog to discover, query, and manage certified datasets.5 concepts · 5 pagesOpen →Use the Catalog Explorer to tag a data asset and view its lineage.2 concepts · 3 pagesOpen →Perform data cleaning on Unity Catalog Tables in SQL, including removing invalid data or handling missing values.4 concepts · 2 pagesOpen →
3Section 3: Importing Data28 questions
Explain the approaches for bringing data into Databricks, covering ingestion from S3, data sharing with external systems via Delta Sharing, API-driven data intake, the Auto Loader feature, and Marketplace.5 concepts · 4 pagesOpen →Use the Databricks Workspace UI to upload a data file to the platform.3 concepts · 2 pagesOpen →
4Section 4: Executing queries using Databricks SQL and Databricks SQL Warehouses177 questions
Utilize Databricks Assistant within a Notebook or SQL Editor to facilitate query writing and debugging.4 concepts · 3 pagesOpen →Explain the role a SQL Warehouse plays in query execution.6 concepts · 5 pagesOpen →Querying cross-system analytics by joining data from a Delta table and a federated data source.6 concepts · 5 pagesOpen →Create a materialized view, including knowing when to use Streaming Tables and Materialized Views, and differentiate between dynamic and materialized views.8 concepts · 5 pagesOpen →Perform aggregate operations such as count, approximate count distinct, mean, and summary statistics.6 concepts · 4 pagesOpen →Write queries to combine tables using various join operations (inner, left, right, and so on) with single or multiple keys, as well as set operations like union and union all, including the differences between the joins (inner, left, right, and so on).15 concepts · 6 pagesOpen →Perform sorting and filtering operations on a table.4 concepts · 2 pagesOpen →Create managed tables and external tables, including creating tables by joining data from multiple sources (e.g., CSV, Parquet, Delta tables) to create unified datasets, including Unity Catalog.10 concepts · 4 pagesOpen →Use Delta Lake's time travel to access and query historical data versions.7 concepts · 5 pagesOpen →
5Section 5: Analyzing Queries137 questions
Understand the Features, Benefits, and Supported Workloads of Photon.5 concepts · 5 pagesOpen →Identify poorly performing queries in the Databricks Intelligence platform, such as Query Insights, Query Profiler log, etc.7 concepts · 6 pagesOpen →Utilize Delta Lake to audit and view history, validate results, and compare historical results or trends.7 concepts · 5 pagesOpen →Utilize query history and caching to reduce development time and query latency8 concepts · 6 pagesOpen →Apply Liquid Clustering to improve query speed when filtering large tables on specific columns.5 concepts · 5 pagesOpen →Fix a query to achieve the desired results.9 concepts · 4 pagesOpen →
6Section 6: Working with Dashboards and Visualizations in Databricks137 questions
Build dashboards using AI/BI Dashboards, including multi-tabs/page layouts, multiple data sources/datasets, and widgets (visualizations, text, images).12 concepts · 7 pagesOpen →Create visualizations in notebooks and the SQL editor.5 concepts · 3 pagesOpen →Work with parameters in SQL queries and dashboards, including defining, configuring, and testing parameters.3 concepts · 4 pagesOpen →Configure permissions through the UI to share dashboards with workspace users/groups, external users through shareable links, and embed dashboards in external apps.3 concepts · 3 pagesOpen →Schedule an automatic dashboard refresh.6 concepts · 3 pagesOpen →Configure an alert with a desired threshold and destination.4 concepts · 3 pagesOpen →Identify the effective visualization type to communicate insights clearly.10 concepts · 6 pagesOpen →
7Section 7: Developing, Sharing, and Maintaining AI/BI Genie spaces89 questions
Describe the purpose, key features, and components of AI/BI Genie spaces.3 concepts · 5 pagesOpen →Create Genie spaces by defining reasonable sample questions and domain-specific instructions, choosing SQL warehouses, curating Unity Catalog datasets (tables, views...), and vetting queries as Trusted Assets.5 concepts · 4 pagesOpen →Assign permissions via the UI and distribute Genie spaces using embedded links and external app integrations.6 concepts · 5 pagesOpen →Optimize AI/BI Genie spaces by tracking user questions, response accuracy, and feedback; updating instructions and trusted assets based on stakeholder input; validating accuracy with benchmarks; refreshing Unity Catalog metadata.7 concepts · 6 pagesOpen →
8Section 8: Data Modeling with Databricks SQL43 questions
9Section 9: Securing Data68 questions
Use Unity Catalog roles and sharing settings to ensure workspace objects are secure.3 concepts · 4 pagesOpen →Understand how the 3-level namespace(Catalog / Schema / Tables or Volumes) works in the Unity Catalog.6 concepts · 6 pagesOpen →Apply best practices for storage and management to ensure data security, including table ownership and PII protection.8 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.
Describe the core components of the Databricks Intelligence Platform, including Mosaic AI, DeltaLive tables, Lakeflow Jobs, Data Intelligence Engine, Delta Lake, Unity Catalog, and Databricks SQL.7 pagesOpen →
Free in front of it
34 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
817 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.
817questions
180pages
39objectives
Freealways
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1Section 1: Understanding of Databricks Data Intelligence Platform95 questions
2Section 2: Managing Data43 questions
3Section 3: Importing Data28 questions
4Section 4: Executing queries using Databricks SQL and Databricks SQL Warehouses177 questions
5Section 5: Analyzing Queries137 questions
6Section 6: Working with Dashboards and Visualizations in Databricks137 questions
7Section 7: Developing, Sharing, and Maintaining AI/BI Genie spaces89 questions
8Section 8: Data Modeling with Databricks SQL43 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.
245concepts
817free 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
245concepts 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 — 39 objectives, 245 concepts written under them, and free questions against every one. When Databricks changes the outline, this page changes with it.
39objectives on the outline→245concepts written→817free 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.