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APACHE-SPARK-DEVELOPER Exam Topics and Domains
APACHE-SPARK-DEVELOPER is organized into 7 weighted domains. Expect to work with DataFrame API, Catalyst Optimizer, DataFrameReader, DataFrameWriter, and more.
Apache Spark Architecture and Components
Spark Architecture Fundamentals
- Identify the advantages and challenges of implementing Spark
- Identify the role of core components of Apache Spark Architecture
- Describe the architecture of Apache Spark including DataFrame and Dataset concepts
Execution Patterns
- Explain the Apache Spark Architecture execution hierarchy
- Configure Spark partitioning in distributed data processing
- Describe the execution patterns of the Apache Spark engine
Spark Modules
Identify the features of the Apache Spark Modules
Using Spark SQL
Data Sources and File Formats
- Utilize common data sources to efficiently read from and write to Spark DataFrames
- Execute SQL queries directly on files
- Access different file formats using SparkSQL
Tables and Views
- Save data to persistent tables while applying sorting and partitioning
- Register DataFrames as temporary views in Spark SQL
Developing Apache Spark DataFrame/DataSet API Applications
DataFrame Manipulations
- Manipulate columns, rows, and table structures
- Perform data deduplication and validation operations on DataFrames
Aggregations and Analytics
- Perform aggregate operations on DataFrames
- Manipulate and utilize Date data type
Combining DataFrames
Combine DataFrames with operations such as joins and unions
Advanced Operations
- Create and invoke user-defined functions
- Describe different types of variables in Spark
- Describe the purpose and implementation of broadcast joins
DataFrame Management
- Manage input and output operations
- Perform operations on DataFrames
Troubleshooting and Tuning Apache Spark DataFrame API Applications
Performance Tuning
- Implement performance tuning strategies
- Optimize cluster utilization
- Describe Adaptive Query Execution and its benefits
Monitoring and Logging
- Perform logging and monitoring of Spark applications
- Diagnose out-of-memory errors and cluster underutilization
Structured Streaming
Streaming Fundamentals
- Explain the Structured Streaming engine in Spark
- Create and write Streaming DataFrames and Streaming Datasets
Streaming Operations
- Perform basic operations on Streaming DataFrames
- Perform Streaming Deduplication in Structured Streaming
Using Spark Connect to deploy applications
Spark Connect
Describe the features of Spark Connect
Deployment Modes
Describe the different deployment mode types in Apache Spark environment
Using Pandas API on Apache Spark
Pandas API on Spark
- Explain the advantages of using Pandas API on Spark
- Create and invoke Pandas UDF
How do I earn this certification?
Passing APACHE-SPARK-DEVELOPER earns the Databricks Certified Associate Developer for Apache Spark certification. It sits in the Data Engineering track.
- data-engineer-associate - Databricks Certified Data Engineer Associate
- data-analyst-associate - Databricks Certified Data Analyst Associate
- ml-associate - Databricks Certified Machine Learning Associate
- data-engineer-professional - Databricks Certified Data Engineer Professional
- ml-professional - Databricks Certified Machine Learning Professional
- data-analyst-associate - Databricks Certified Data Analyst AssociateSQL and visualization focus for analytics roles
- ml-associate - Databricks Certified Machine Learning Associate ML pipeline and model deployment focus
- generative-ai-engineer - Databricks Certified Generative AI Engineer Associate Focus on LLMs and generative AI applications
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
Use the official PlanetCert Practice Test alongside the study plan below to prepare efficiently for APACHE-SPARK-DEVELOPER.
What's changed on this exam?
- ACTIVE
- Last content update: 2025-09-15
- Announcement date: 2024-04-16
- Structured Streaming 3.5+ Now 10% of exam content, includes watermarking and checkpointing • Release date: 2024-06-01
- Pandas API on Spark 3.4+ 5% of exam content, focuses on Pandas UDF creation • Release date: 2024-01-01
- Adaptive Query Execution (AQE) 3.0+ Part of performance tuning section (10% of exam) • Release date: 2020-06-01
- Spark Connect 3.4+ 5% of exam content, deployment modes focus • Release date: 2023-04-01
Who should take this exam?
This exam is typically taken by Data Engineers and Software Engineers working with big data.
- 6+ months of hands-on experience with Apache Spark
- Experience with Python programming
- Understanding of distributed computing concepts