DCAD Exam Information and Outline
Databricks Certified Associate Developer for Apache Spark 3.0
DCAD Exam Syllabus & Study Guide
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official DCAD exam objectives. This course outline serves as your roadmap.
The information below reflects the 2026 syllabus defined by
Databricks.
Below are complete topics detail with latest syllabus and course outline, that will help you good knowledge about exam objectives and topics that you have to prepare. These contents are covered in questions and answers pool of exam.
Exam Details for DCAD Databricks Certified Associate Developer for Apache Spark 3.0:
Number of Questions: The exam consists of approximately 60 multiple-choice and multiple-select questions.
Time Limit: The total time allocated for the exam is 90 minutes (1 hour and 30 minutes).
Passing Score: To pass the exam- you must achieve a minimum score of 70%.
Exam Format: The exam is conducted online and is proctored. You will be required to answer the questions within the allocated time frame.
Course Outline:
1. Spark Basics:
- Understanding Apache Spark architecture and components
- Working with RDDs (Resilient Distributed Datasets)
- Transformations and actions in Spark
2. Spark SQL:
- Working with structured data using Spark SQL
- Writing and executing SQL queries in Spark
- DataFrame operations and optimizations
3. Spark Streaming:
- Real-time data processing with Spark Streaming
- Windowed operations and time-based transformations
- Integration with external systems and sources
4. Spark Machine Learning (MLlib):
- Introduction to machine learning with Spark MLlib
- Feature extraction and transformation in Spark MLlib
- Model training and evaluation using Spark MLlib
5. Spark Graph Processing (GraphX):
- Working with graph data in Spark using GraphX
- Graph processing algorithms and operations
- Analyzing and visualizing graph data in Spark
6. Spark Performance Tuning and Optimization:
- Identifying and resolving performance bottlenecks in Spark applications
- Spark configuration and tuning techniques
- Optimization strategies for Spark data processing
Exam Objectives:
1. Understand the fundamentals of Apache Spark and its components.
2. Perform data processing and transformations using RDDs.
3. Utilize Spark SQL for structured data processing and querying.
4. Implement real-time data processing using Spark Streaming.
5. Apply machine learning techniques with Spark MLlib.
6. Analyze and process graph data using Spark GraphX.
7. Optimize and tune Spark applications for improved performance.
Exam Syllabus:
The exam syllabus covers the following topics:
1. Spark Basics
- Apache Spark architecture and components
- RDDs (Resilient Distributed Datasets)
- Transformations and actions in Spark
2. Spark SQL
- Spark SQL and structured data processing
- SQL queries and DataFrame operations
- Spark SQL optimizations
3. Spark Streaming
- Real-time data processing with Spark Streaming
- Windowed operations and time-based transformations
- Integration with external systems
4. Spark Machine Learning (MLlib)
- Introduction to machine learning with Spark MLlib
- Feature extraction and transformation
- Model training and evaluation
5. Spark Graph Processing (GraphX)
- Graph data processing in Spark using GraphX
- Graph algorithms and operations
- Graph analysis and visualization
6. Spark Performance Tuning and Optimization
- Performance bottlenecks and optimization techniques
- Spark configuration and tuning
- Optimization strategies for data processing