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Updated: Jul 30, 2026
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Databricks Associate-Developer-Apache-Spark Exam Overview:
| Certification Vendor: | Databricks |
| Exam Name: | Databricks Certified Associate Developer for Apache Spark |
| Exam Number: | Databricks Certified Associate Developer for Apache Spark |
| Exam Format: | Multiple Choice |
| Real Exam Qty: | 45 |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Exam Price: | USD 200 |
| Passing Score: | 70% |
| Exam Duration: | 90 minutes |
| Related Certifications: | Databricks Certified Professional Data Engineer Databricks Certified Data Engineer Associate |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark Sample Questions |
| Exam Way: | Online proctored via Databricks/Testing partner or at authorized test centers |
| Pre Condition: | No formal prerequisites; recommended hands-on experience with Apache Spark and Databricks environment. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
The Databricks Associate-Developer-Apache-Spark exam consists of multiple-choice questions and is designed to test the candidate's knowledge of Spark programming concepts, Spark SQL, Spark Streaming, and Spark MLlib. Associate-Developer-Apache-Spark exam is 90 minutes long and consists of 60 questions. Associate-Developer-Apache-Spark exam is conducted online and can be taken from anywhere in the world.
Databricks Certified Associate Developer for Apache Spark 3.0 is a certification exam that evaluates an individual's knowledge and skills in developing Spark applications using Databricks. Databricks Certified Associate Developer for Apache Spark 3.0 Exam certification is designed for software developers, data engineers, and data scientists who are interested in validating their skills and knowledge in developing Spark applications using Databricks.
Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Tuning | 10% | - Spark UI and Bottlenecks - Caching and Shuffle Optimization - Debugging Spark Applications |
| Topic 2: Structured Streaming | 10% | - Watermarks and Triggers - Stateful Processing Basics - Streaming Concepts |
| Topic 3: DataFrame and Dataset API Applications | 30% | - Joins and Transformations - Column Selection and Manipulation - Filtering, Sorting and Aggregation |
| Topic 4: Using Spark Connect and Pandas API on Spark | 10% | - Pandas API Integration - Remote Connectivity |
| Topic 5: Using Spark SQL | 20% | - Window Functions - SQL Queries and Operations - Performance Optimization |
| Topic 6: Apache Spark Architecture and Components | 20% | - Partitioning and Fault Tolerance - Cluster Mode and Execution Hierarchy - Driver and Executor Roles |
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