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Updated: Aug 27, 2026
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Databricks Associate-Developer-Apache-Spark Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.0 Exam |
| Exam Number: | Associate-Developer-Apache-Spark |
| Exam Duration: | 120 minutes |
| Exam Price: | $200 USD |
| Exam Format: | Multiple Choice, Multiple Select |
| Passing Score: | 70% |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 60 |
| Related Certifications: | Databricks Certified Professional Data Engineer Databricks Certified Associate Data Engineer |
| Recommended Training: | Databricks Exam Guide Databricks Academy - Apache Spark for Developers |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark Sample Questions |
| Exam Way: | Online proctored or in-person at authorized test centers |
| Pre Condition: | Basic programming experience in Python or Scala; working knowledge of SQL; recommended 6+ months of hands-on Spark experience |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
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How to become Databricks Associate Developer Apache Spark certified
The Databricks Associate Developer Apache Spark certification is a great opportunity to get hands-on experience with the most popular big data platform. It's a two-day certification course that teaches you how to use the latest version of Apache Spark. Databricks Associate Developer Apache Spark exam dumps are the key of success.
To create applications that run on the Databricks platform. You'll learn how to use the Spark shell to interact with the data stored in Databricks, the Databricks DataFrame API, and the Databricks Query Language (DQL). You'll also gain exposure to a variety of machine learning and data mining algorithms that can be used to solve real-world problems.
Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Architecture and Components | 28% | - Fault tolerance and shuffle operations - Spark execution model and hierarchy - Lazy evaluation, transformations vs actions - Driver and executor architecture |
| Topic 2: Structured Streaming | 8% | - Output modes and triggers - Streaming concepts and operations |
| Topic 3: Using Spark SQL | 12% | - Working with views and tables - Supported file formats and data sources - Querying data with Spark SQL |
| Topic 4: Developing DataFrame API Applications | 44% | - User-defined functions (UDFs) - Filtering, sorting, aggregation - Column and row manipulation - Reading/writing data and partitioning - Handling missing data and duplicates |
| Topic 5: Troubleshooting and Tuning | 8% | - Query plans and execution analysis - Performance optimization |
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