Databricks Certified Associate Developer for Apache Spark

Know the domains, time limit, question pattern and official registration path before studying.

• Read the official Databricks exam page and PySpark.in exam summary.
• Note the seven domains and their weightage.
• Set a target exam date 4 to 6 weeks away if you already know Spark basics.

Become confident with driver, executors, jobs, stages, tasks, lazy evaluation and shuffles.

• Revise SparkSession, cluster execution, partitions and transformations vs actions.
• Practice questions on stage boundaries, caching and fault tolerance.
• Use Spark UI concepts to understand why a query is slow.

Practise the highest-weighted area: selecting, filtering, joins, aggregation, schemas and writes.

• Solve PySpark DataFrame challenges daily.
• Practise column expressions, null handling, UDFs, joins and partitioned writes.
• Rewrite common SQL logic using DataFrame APIs.

Handle SQL questions quickly: temp views, functions, joins, windows and query plans.

• Practise Spark SQL syntax with realistic tables.
• Review built-in functions and why they are preferred over Python UDFs.
• Use explain plans to connect SQL with Spark execution.

Round out lower-weighted but important topics: AQE, skew, Structured Streaming, Spark Connect and Pandas API on Spark.

• Review shuffle partitions, broadcast joins, salting and repartition vs coalesce.
• Study Adaptive Query Execution (AQE) for runtime partition, join and skew optimization.
• Learn watermarking, checkpointing, output modes and foreachBatch basics.
• Understand Spark Connect as a decoupled client-server architecture.

Move from learning mode to exam mode with timed mocks and mistake review.

• Start with the 10-question guide demo, then take the 20-question mixed mock.
• Take the paid 60-question practice set only after your weak areas are reviewed.
• Repeat until you score 70%+ twice without guessing.