Associate Developer for Apache Spark
6
270
45 questions
90 minutes
Not published by Databricks (pass/fail result)
Multiple choice
Apache Spark Architecture and Components | Driver, executors and cluster managers, the execution hierarchy, lazy evaluation, caching and storage levels, partitioning and shuffles (20%) Using Spark SQL | Reading and writing JDBC and file sources, SQL on files, save modes, partitioned and bucketed tables, and temporary views (20%) Developing Apache Spark DataFrame/DataSet API Applications | Column and row manipulation, deduplication, aggregations, dates, joins and unions, schemas, UDFs, broadcast variables and accumulators (30%) Troubleshooting and Tuning Apache Spark DataFrame API Applications | Repartitioning and coalescing, data skew, shuffles, Adaptive Query Execution, and driver and executor logs (10%) Structured Streaming | Micro-batch processing, exactly-once semantics, output modes and sinks, windows, watermarks and streaming deduplication (10%) Using Spark Connect to deploy applications | Spark Connect features and the client, cluster and local deployment modes (5%) Using Pandas API on Apache Spark | Advantages of the Pandas API on Spark, and creating and invoking pandas UDFs (5%)
Data engineers preparing for the Databricks Certified Associate Developer for Apache Spark exam PySpark developers building DataFrame and Spark SQL applications ETL and analytics engineers moving pipelines onto Apache Spark Data professionals validating their Structured Streaming and Spark tuning skills
Using Spark Connect to deploy applications
Which of the following is NOT available to a PySpark client connected through Spark Connect?
DataFrame transformations such as filter() and groupBy()
The SparkContext and RDD APIs, for example spark.sparkContext.parallelize()
Queries run with spark.sql()
Structured Streaming with readStream and writeStream
B
Spark Connect exposes the DataFrame, SQL and Structured Streaming APIs to a thin client, which sends unresolved plans to the server over gRPC. The SparkContext and RDD APIs need direct access to the driver JVM, so they are not available to a Connect client.
Updated 2026Instant accessLifetime access

Databricks Certified Associate Developer for Apache Spark Practice Tests: 6 Practice Tests and 270 Questions

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These practice tests are for data engineers, PySpark developers, and ETL and analytics engineers preparing for the Databricks Certified Associate Developer for Apache Spark exam. They are a quick way to check your readiness before you book the exam.

You get 6 full-length practice tests with 45 questions each, 270 questions in total, sized like the real exam. Every question explains why the right answer is right and why the others are wrong, and each one is mapped to a section of the official exam guide. The series was rebuilt for 2026 against the exam guide live since October 2025. Every answer key was re-checked, questions with more than one correct option were fixed, retired content such as Mesos was replaced, and new questions cover Spark Connect, the Pandas API on Spark and pandas UDFs, SQL on files, Adaptive Query Execution and streaming deduplication.

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Databricks Certified Spark Developer Mock Tests

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