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Databricks Associate Developer for Apache Spark Study Guide

Databricks Certified Associate Developer for Apache Spark

The Databricks Certified Associate Developer for Apache Spark exam is for developers who complete basic Spark DataFrame tasks in Python. It covers the Spark architecture and execution model, Spark SQL and data sources, the DataFrame API for selecting, joining, aggregating and writing data, troubleshooting and tuning with AQE, Structured Streaming, Spark Connect and the Pandas API on Spark.

  • Databricks certification
  • Associate level
  • 7 exam sections

This guide is free, with no sign-up. It explains every Databricks Associate Developer for Apache Spark exam topic in plain English, one chapter at a time. Read them in order or jump straight to the topic you need.

  • 7chapters
  • 20topics
  • 6diagrams and exhibits
  • 1 h 25 minfast track
Start with Apache Spark Architecture and Components
  • Every topic follows the same simple pattern: what it is, how it works, common mistakes
  • Diagrams and real command output where a picture explains it best
  • Exam tips and worked subnetting and IPv6 practice, with answers

How the exam is weighted

Each domain is worth this share of the exam, so spend your study time in proportion. Select a domain to open its chapter.

Chapters

Open a chapter to see its topics and how long each takes.

1Apache Spark Architecture and Components4 topics · 17 min fast trackSpark Architecture and Components for the Databricks Spark Developer exam: cluster roles, caching, jobs and stages, partitions and lazy evaluation.
  1. 1Spark Benefits, Challenges and Core Components4 min
  2. 2DataFrames, SparkSession, Caching and Garbage Collection4 min
  3. 3Execution Hierarchy, Partitions and Shuffles5 min
  4. 4Actions, Transformations, Lazy Evaluation and Spark Modules4 min
Read the chapter overview →
2Using Spark SQL3 topics · 12 min fast trackUsing Spark SQL for the Databricks Spark Developer exam: JDBC and file sources, SQL on files, save modes, persistent tables and temporary views.
  1. 1Reading and Writing JDBC and File Sources4 min
  2. 2Querying Files Directly and Save Modes4 min
  3. 3Persistent Tables and Temporary Views4 min
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3Developing Apache Spark DataFrame/DataSet API Applications6 topics · 26 min fast trackColumns, joins, dates, I/O, UDFs and shared variables.
  1. 1Columns, Rows and Table Structures4 min
  2. 2Deduplication, Validation and Aggregation4 min
  3. 3Dates, Sorting and DataFrame Conversion4 min
  4. 4Joins, Unions and Broadcast Joins5 min
  5. 5Reading and Writing DataFrames with Schemas4 min
  6. 6UDFs, Broadcast Variables and Accumulators5 min
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4Troubleshooting and Tuning2 topics · 9 min fast trackPartitions, skew, shuffles, AQE, driver and executor logs.
  1. 1Performance Tuning, Partitioning, Skew and AQE5 min
  2. 2Logging and Monitoring Spark Applications4 min
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5Structured Streaming3 topics · 12 min fast trackMicro batches, exactly once, output modes, windows, watermarks and deduplication.
  1. 1The Structured Streaming Engine, Output Modes and Sinks4 min
  2. 2Operations on Streams - Selection, Windows, Aggregation and Watermarks4 min
  3. 3Streaming Deduplication With and Without a Watermark4 min
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6Using Spark Connect to Deploy Applications1 topic · 5 min fast trackThin client, gRPC, client mode, cluster mode and local mode.
  1. 1Spark Connect and Deployment Modes5 min
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7Using Pandas API on Spark1 topic · 4 min fast trackWhen to use each, how they scale and what they cost.
  1. 1Pandas API on Spark and Pandas UDFs4 min
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