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 Associate Developer for Apache Spark
The Databricks Certified Associate Developer for Apache Spark certification validates your ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session. It is designed for data practitioners who work with Python and want to demonstrate a solid understanding of Spark architecture, components, and common troubleshooting techniques. Earning this credential signals that you can handle real-world Spark development tasks with confidence.
Exam formatMultiple choice
Duration90 minutes
DeliveryDatabricks
Free questions573
Content last reviewed 30 July 2026 · Up to date
The certification
What Databricks Certified Associate Developer for Apache Spark 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
27objectives
168concepts
$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 Associate Developer for Apache Spark certification exam assesses your understanding of Apache Spark Architecture and Components, and your ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session. These tasks include selecting, renaming, and manipulating columns; filtering, dropping, sorting, and aggregating rows; handling missing data; combining, reading, writing, and partitioning DataFrames with schemas; and working with UDFs and Spark SQL functions.
The exam also covers the basics of Spark architecture, including execution and deployment modes, the execution hierarchy, fault tolerance, garbage collection, lazy evaluation, shuffling, and the use of actions and broadcasting. You will also be tested on Structured Streaming, Spark Connect, and common troubleshooting and tuning techniques. Passing this exam demonstrates that you can complete basic Spark DataFrame tasks using Python, making you a valuable asset in data engineering and analytics roles.
Who it’s for
This certification is for data engineers, data scientists, and analytics professionals who work with Apache Spark and want to validate their ability to perform basic data manipulation tasks using the Spark DataFrame API. It is ideal for individuals who have hands-on experience with Spark and Python and are looking to formalize their skills. You should be comfortable writing Python code and have a foundational understanding of Spark's architecture and components. The exam is designed for those who are early in their Spark journey but have enough practical experience to apply the concepts in real-world scenarios.
Recommended experience
6+ months of hands-on experience performing the tasks outlined in the exam guide. Experience with the Spark DataFrame API for data manipulation; Understanding of Spark architecture and components; Familiarity with Python programming; Experience with troubleshooting and tuning Spark applications
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
Apache Spark Architecture and Components
- Identify the advantages and challenges of implementing Spark.
- Identify the role of core components of Apache Spark™'s Architecture, including cluster, driver node, worker nodes/executors, CPU cores, and memory.
- Describe the architecture of Apache Spark™, including DataFrame and Dataset concepts, SparkSession lifecycle, caching, storage levels, and garbage collection.
- Explain the Apache Spark™ Architecture execution hierarchy..
- Configure Spark partitioning in distributed data processing, including shuffles and partitions
- Describe the execution patterns of the Apache Spark™ engine, including actions, transformations, and lazy evaluation.
- Identify the features of the Apache Spark Modules, including Core, Spark SQL, DataFrames, Pandas API on Spark, Structured Streaming, and MLib.
Using Spark SQL
- Utilize common data sources such as JDBC, files, etc., to efficiently read from and write to Spark DataFrames using Spark SQL, including overwriting and partitioning by column.
- Execute SQL queries directly on files, including ORC Files, JSON Files, CSV Files, Text Files, and Delta Files, and understand the different save modes for outputting data in Spark SQL.
- Save data to persistent tables while applying sorting and partitioning to optimize data retrieval.
- Register DataFrames as temporary views in Spark SQL, allowing them to be queried with SQL syntax.
Developing Apache Spark™ DataFrame/DataSet API Applications
- DataFrame Transformations
- Data Aggregation and Joins
- Input/Output Operations
- User-Defined Functions and Stateful Operations
- Shared Variables and Broadcast Joins
Troubleshooting and Tuning Apache Spark DataFrame API Applications.
- Implement performance tuning strategies & optimize cluster utilization, including partitioning, repartitioning, coalescing, identifying data skew, and reducing shuffling
- Describe Adaptive Query Execution (AQE) and its benefits.
- Perform logging and monitoring of Spark applications - publish, customize, and analyze Driver logs and Executor logs to diagnose out-of-memory errors, cluster underutilization, etc.
Structured Streaming
- Explain the Structured Streaming engine in Spark, including its functions, programming model, micro-batch processing, exactly-once semantics, and fault tolerance mechanisms.
- Create and write Streaming DataFrames and Streaming Datasets, including the basic output modes and output sinks.
- Perform basic operations on Streaming DataFrames and Streaming Datasets, such as selection, projection, window and aggregation.
- Perform Streaming Deduplication in Structured Streaming, both with and without watermark usage.
Using Spark Connect to deploy applications
- Describe the features of Spark Connect.
- Describe the different deployment mode types (Client, Cluster, Local) in the Apache Spark™ environment.
Using Pandas API on Spark
- Explain the advantages of using Pandas API on Spark.
- Create and invoke Pandas UDF.
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 Associate Developer for Apache Spark
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 Associate Developer for Apache Spark
Credential earnedDatabricks Certified Associate Developer for Apache Spark Associate level certification Renewal and maintenance
Certifications need to be renewed by retaking the exam. The validity period is 2 years. 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 this exam relate to the retired Databricks Certified Hadoop Migration Architect exam?
The Hadoop Migration Architect exam was retired as of August 1, 2024, and is not a prerequisite or replacement for this exam. This exam focuses on Apache Spark development skills.
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 to prepare.
Is there a hands-on or lab component in this exam?
No. The exam consists of multiple-choice questions only. There is no hands-on lab component.
What is the retake policy if I fail the exam?
Databricks does not publish a specific retake policy for this exam. Please refer to the exam terms and conditions for details.
What job roles does this certification map to?
This certification is relevant for data engineers, data scientists, and analytics professionals who work with Apache Spark and Python.
Can I recertify by passing a different Databricks exam?
No. Recertification requires retaking the current version of this exam. Passing a different exam does not renew this certification.
Are there any regional restrictions for taking this exam?
The exam is available online and at test centers. Regional availability may vary; check the registration platform for options in your area.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 573 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
27objectives
168concepts
125free 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
1Apache Spark Architecture and Components172 questions
Identify the advantages and challenges of implementing Spark.2 concepts · 4 pagesOpen →Identify the role of core components of Apache Spark™'s Architecture, including cluster, driver node, worker nodes/executors, CPU cores, and memory.6 concepts · 6 pagesOpen →Describe the architecture of Apache Spark™, including DataFrame and Dataset concepts, SparkSession lifecycle, caching, storage levels, and garbage collection.6 concepts · 5 pagesOpen →Explain the Apache Spark™ Architecture execution hierarchy..7 concepts · 6 pagesOpen →Configure Spark partitioning in distributed data processing, including shuffles and partitions8 concepts · 6 pagesOpen →Describe the execution patterns of the Apache Spark™ engine, including actions, transformations, and lazy evaluation.5 concepts · 4 pagesOpen →Identify the features of the Apache Spark Modules, including Core, Spark SQL, DataFrames, Pandas API on Spark, Structured Streaming, and MLib.6 concepts · 5 pagesOpen →
2Using Spark SQL73 questions
Utilize common data sources such as JDBC, files, etc., to efficiently read from and write to Spark DataFrames using Spark SQL, including overwriting and partitioning by column.7 concepts · 4 pagesOpen →Execute SQL queries directly on files, including ORC Files, JSON Files, CSV Files, Text Files, and Delta Files, and understand the different save modes for outputting data in Spark SQL.7 concepts · 4 pagesOpen →Save data to persistent tables while applying sorting and partitioning to optimize data retrieval.7 concepts · 5 pagesOpen →Register DataFrames as temporary views in Spark SQL, allowing them to be queried with SQL syntax.5 concepts · 3 pagesOpen →
3Developing Apache Spark™ DataFrame/DataSet API Applications109 questions
4Troubleshooting and Tuning Apache Spark DataFrame API Applications.90 questions
Implement performance tuning strategies & optimize cluster utilization, including partitioning, repartitioning, coalescing, identifying data skew, and reducing shuffling7 concepts · 6 pagesOpen →Describe Adaptive Query Execution (AQE) and its benefits.8 concepts · 6 pagesOpen →Perform logging and monitoring of Spark applications - publish, customize, and analyze Driver logs and Executor logs to diagnose out-of-memory errors, cluster underutilization, etc.10 concepts · 7 pagesOpen →
5Structured Streaming73 questions
Explain the Structured Streaming engine in Spark, including its functions, programming model, micro-batch processing, exactly-once semantics, and fault tolerance mechanisms.5 concepts · 5 pagesOpen →Create and write Streaming DataFrames and Streaming Datasets, including the basic output modes and output sinks.4 concepts · 3 pagesOpen →Perform basic operations on Streaming DataFrames and Streaming Datasets, such as selection, projection, window and aggregation.7 concepts · 3 pagesOpen →Perform Streaming Deduplication in Structured Streaming, both with and without watermark usage.6 concepts · 5 pagesOpen →
6Using Spark Connect to deploy applications35 questions
7Using Pandas API on Spark21 questions
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.
Identify the advantages and challenges of implementing Spark.4 pagesOpen →
Free in front of it
20 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
573 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.
573questions
125pages
27objectives
Freealways
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1Apache Spark Architecture and Components172 questions
2Using Spark SQL73 questions
3Developing Apache Spark™ DataFrame/DataSet API Applications109 questions
4Troubleshooting and Tuning Apache Spark DataFrame API Applications.90 questions
5Structured Streaming73 questions
6Using Spark Connect to deploy applications35 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.
168concepts
573free 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
168concepts 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 — 27 objectives, 168 concepts written under them, and free questions against every one. When Databricks changes the outline, this page changes with it.
27objectives on the outline→168concepts written→573free 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.