Databricks Spark Developer Associate
Databricks Certified Associate Developer for Apache Spark
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ExploreAbout this exam
The Databricks Certified Associate Developer for Apache Spark is a proctored, multiple-choice certification that assesses understanding of Apache Spark architecture and components plus the ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session. The published scope includes 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. It also covers architecture basics such as execution and deployment modes, the execution hierarchy, fault tolerance, garbage collection, lazy evaluation, shuffling, actions and broadcasting, along with Structured Streaming, Spark Connect, and common troubleshooting and tuning techniques. The exam targets practitioners who want to demonstrate they can complete basic Spark DataFrame tasks in Python; Databricks states there are no prerequisites, though related training is highly recommended, and recommends 6+ months of hands-on experience performing the tasks outlined in the exam guide. The scored exam contains 45 questions in 90 minutes, costs $200, is delivered online or at a test center in English, and permits no test aides. Certification is valid for two years.
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About Databricks Spark Developer Associate preparation
What is the exam format and how many questions are there?
It is a proctored certification with 45 scored questions in multiple-choice format, completed in 90 minutes. No test aides are allowed. Delivery is online or at a test center, and the exam is offered in English only. Exams may also include unscored items, which are not identified and do not affect your score; extra time is factored in for them.
Is there a prerequisite or recommended experience?
Databricks lists no prerequisites, but says related training is highly recommended. It also recommends 6+ months of hands-on experience performing the tasks outlined in the exam guide. In practice, that means comfort writing DataFrame transformations, joins, aggregations, and schema-handling code in Python before sitting the exam.
What is the passing score and how much does it cost?
The registration fee is $200, payable when you register through the exam delivery platform. Databricks does not publish a passing score on the certification page, so no reliable pass threshold is available; plan to aim well above a bare minimum by covering every domain rather than guessing at a cut score.
What does the exam cover, percentage-wise?
The published breakdown is Apache Spark Architecture and Components 20%, Using Spark SQL 20%, Developing DataFrame/DataSet API Applications 30%, Troubleshooting and Tuning 10%, Structured Streaming 10%, Using Spark Connect to deploy applications 5%, and Using Pandas API on Apache Spark 5%. The DataFrame domain is the largest, so weight your practice there.
How long is the certification valid, and how do I recertify?
The validity period is two years. Recertification is required every two years to maintain certified status, and to recertify you must take the current version of the exam. Databricks advises reviewing the exam guide again and using the preparation steps to close any gaps before your recertification attempt.
What language will the code questions use?
Databricks states that all learning code and code snippets within the exam will be in Python. Expect DataFrame API, Spark SQL, and function usage expressed in PySpark. If you normally work in Scala, practice translating common transformations, aggregations, and UDF patterns into PySpark syntax before exam day.
What is the best order to study the domains?
Start with architecture, since lazy evaluation, shuffles, actions, and broadcasting explain why later tuning advice works. Then drill the DataFrame API in Python, followed by Spark SQL, because the two overlap heavily. Finish with tuning, Structured Streaming, Spark Connect, and the Pandas API, which carry smaller weights but are easy points once the fundamentals are solid.
What are common failure modes on this exam?
Candidates often lose points on architecture details rather than syntax: how shuffles arise, what triggers evaluation, and how actions differ from transformations. Others miss streaming semantics, such as output modes and trigger behavior, or confuse the Pandas API on Spark with local pandas. Breadth across all seven domains matters more than depth in one.
How do I register and prepare using official resources?
Register by logging in or creating an account on the exam delivery platform. To prepare, review the official exam guide to see what is covered, take the related training it lists, use the AI Prep Guide, check the technical requirements with a system check, and then revisit the guide to identify remaining gaps before your exam.