Databricks Certified Associate Developer for Apache Spark

Exam domains

• Developing Apache Spark DataFrame/DataSet API Applications30%

  Build PySpark DataFrame applications using select, filter, withColumn, drop, sort, groupBy, agg, join, union, and window operations, plus schema handling, missing-data functions (na.fill/drop), and partitioned reads/writes across JSON, CSV, Parquet, ORC, and Delta. Author Python and pandas UDFs and apply Spark SQL functions for column-level transformations.

• Using Spark SQL20%

  Use Spark SQL to query and manipulate data with SELECT, JOIN, GROUP BY, window functions, CTEs, and built-in functions across temporary views and catalog tables. Apply DDL/DML on managed and external tables, register UDFs for SQL use, and leverage the Catalyst optimizer and AQE for efficient query planning.

• Apache Spark Architecture and Components20%

  Understand the Apache Spark execution model including the driver, executors, cluster managers, jobs/stages/tasks hierarchy, and partitions. Cover lazy evaluation, narrow vs. wide transformations and shuffles, fault tolerance via lineage, SparkSession/SparkContext entry points, deployment modes, and the Catalyst optimizer with Adaptive Query Execution (AQE).

• Structured Streaming10%

Sources

Questions are grounded in 50 references from official and authoritative materials.

• Spark Connect thin client architecture and deployment model
• User-Defined Functions and Pandas UDFs in PySpark
• PySpark DataFrame joins, aggregations, and window functions
• Spark transformations, actions, and lazy evaluation

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