Spark Dev Assoc study guide · 2026

The 7 Spark Dev Assoc domains, explained

7 domains and 32 official objectives. Developing Apache Spark DataFrame/DataSet API Applications carries the most weight at 30%. Each guide explains what the domain covers, how its questions are framed and where candidates lose marks.

Start with Domain 3 See all domains
Questions
45 scored multiple-choice questions (plus up to 10 unscored items)
Time
90 minutes
Passing score
Not published by Databricks
Outline
Databricks Certified Associate Developer for Apache Spark exam guide, August 2026 edition (exam published and guide updated 8 Oct 2026; current)

Exam blueprint

Bar widths follow the official weights. Weight ranges are drawn at their midpoint.

  1. 1 20%
  2. 2 20%
  3. 3 30%
  4. 4 10%
  5. 5 10%
  6. 6 5%
  7. 7 5%
  1. 1 Apache Spark Architecture and Components
  2. 2 Using Spark SQL
  3. 3 Developing Apache Spark DataFrame/DataSet API Applications
  4. 4 Troubleshooting and Tuning Apache Spark DataFrame API Applications
  5. 5 Structured Streaming
  6. 6 Using Spark Connect to Deploy Applications
  7. 7 Using Pandas API on Spark

All 7 domains

32 official objectives 61–85 hrs suggested study

  1. Domain 01 20%

    Apache Spark Architecture and Components

    How a Spark application is organised and executed: the driver and executors, the job, stage and task hierarchy, partitions and shuffles, caching with storage levels, lazy evaluation and the modules that sit on top of Spark Core.

    Inside this domain

    • Identify the advantages and challenges of implementing Spark
    • Identify the role of core components of Apache Spark's Architecture, including cluster, driver node, worker nodes/executors, CPU cores, and memory
    • Describe the architecture of Apache Spark, including DataFrame and Dataset concepts, SparkSession lifecycle, caching, storage levels, and garbage collection
    • Explain the Apache Spark Architecture execution hierarchy
    • and 3 more objectives

    Watch for Every line runs

    Objectives
    7
    Questions est.
    ~9
    Study time est.
    12–16 hrs
  2. Domain 02 20%

    Using Spark SQL

    Reading and writing data with Spark SQL: JDBC and file sources, querying files directly, save modes, partitioned and bucketed persistent tables, and temporary views that make DataFrames queryable with SQL.

    Inside this domain

    • Utilize common data sources such as JDBC, files, etc., to efficiently read from and write to Spark DataFrames using Spark SQL, including overwriting and partitioning by column
    • Execute SQL queries directly on files, including ORC Files, JSON Files, CSV Files, Text Files, and Delta Files, and understand the different save modes for outputting data in Spark SQL
    • Save data to persistent tables while applying sorting and partitioning to optimize data retrieval
    • Register DataFrames as temporary views in Spark SQL, allowing them to be queried with SQL syntax

    Watch for Overwrite is default

    Objectives
    4
    Questions est.
    ~9
    Study time est.
    10–14 hrs
  3. Domain 03 30%

    Developing Apache Spark DataFrame/DataSet API Applications

    The largest section of the exam: everyday PySpark DataFrame work, from column and row manipulation, deduplication and aggregation to date handling, joins and unions, schema-driven I/O, UDFs, shared variables and broadcast joins.

    Inside this domain

    • Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays
    • Perform data deduplication and validation operations on DataFrames
    • Perform aggregate operations on DataFrames such as count, approximate count distinct, and mean, summary
    • Manipulate and utilize Date data type, such as Unix epoch to date string, and extract date component
    • and 6 more objectives

    Watch for Union aligns names

    Objectives
    10
    Questions est.
    ~14
    Study time est.
    18–24 hrs
  4. Domain 04 10%

    Troubleshooting and Tuning Apache Spark DataFrame API Applications

    Making DataFrame applications run well and diagnosing them when they do not: partition tuning, skew and shuffle reduction, Adaptive Query Execution, and reading driver and executor logs for out-of-memory errors and idle resources.

    Inside this domain

    • Implement performance tuning strategies & optimize cluster utilization, including partitioning, repartitioning, coalescing, identifying data skew, and reducing shuffling
    • Describe Adaptive Query Execution (AQE) and its benefits
    • Perform logging and monitoring of Spark applications - publish, customize, and analyze Driver logs and Executor logs to diagnose out-of-memory errors, cluster underutilization, etc.

    Watch for More nodes fix skew

    Objectives
    3
    Questions est.
    ~5
    Study time est.
    7–10 hrs
  5. Domain 05 10%

    Structured Streaming

    Processing unbounded data with the DataFrame API: the micro-batch engine, checkpoints and exactly-once guarantees, streaming sources, output modes and sinks, windowed aggregations, and deduplication with and without watermarks.

    Inside this domain

    • Explain the Structured Streaming engine in Spark, including its functions, programming model, micro-batch processing, exactly-once semantics, and fault tolerance mechanisms
    • Create and write Streaming DataFrames and Streaming Datasets, including the basic output modes and output sinks
    • Perform basic operations on Streaming DataFrames and Streaming Datasets, such as selection, projection, window and aggregation
    • Perform Streaming Deduplication in Structured Streaming, both with and without watermark usage

    Watch for Complete for everything

    Objectives
    4
    Questions est.
    ~5
    Study time est.
    8–11 hrs
  6. Domain 06 5%

    Using Spark Connect to Deploy Applications

    How Spark Connect separates client applications from the Spark driver, what it supports and leaves out, and how client, cluster and local deployment modes decide where the driver runs.

    Inside this domain

    • Describe the features of Spark Connect
    • Describe the different deployment mode types (Client, Cluster, Local) in the Apache Spark environment

    Watch for Connect runs locally

    Objectives
    2
    Questions est.
    ~2
    Study time est.
    3–5 hrs
  7. Domain 07 5%

    Using Pandas API on Spark

    Running pandas-style code at cluster scale with pyspark.pandas, converting between pandas-on-Spark, Spark and pandas DataFrames, and writing vectorised pandas UDFs that use Apache Arrow and Python type hints.

    Inside this domain

    • Explain the advantages of using Pandas API on Spark
    • Create and invoke Pandas UDF

    Watch for Pandas-on-Spark is local

    Objectives
    2
    Questions est.
    ~2
    Study time est.
    3–5 hrs

Study the heaviest domains first

Marks follow weight. This order puts the most marks behind you first, across about 61–85 hrs of study.

  1. 3 Developing Apache Spark DataFrame/DataSet API Applications 30% · 18–24 hrs
  2. 1 Apache Spark Architecture and Components 20% · 12–16 hrs
  3. 2 Using Spark SQL 20% · 10–14 hrs
  4. 4 Troubleshooting and Tuning Apache Spark DataFrame API Applications 10% · 7–10 hrs
  5. 5 Structured Streaming 10% · 8–11 hrs
  6. 6 Using Spark Connect to Deploy Applications 5% · 3–5 hrs
  7. 7 Using Pandas API on Spark 5% · 3–5 hrs

Try one

Practice questionDomain 3: Developing Apache Spark DataFrame/DataSet API Applications

A DataFrame events has a column ts_ms holding epoch time in milliseconds. A developer needs a new column event_date of DateType. Which expression produces it correctly?

One trap from every domain

The answer that looks right, and the clue that gives it away.

  • Domain 1

    Every line runs

    Clue The snippet has several transformations and only one action at the end.

  • Domain 2

    Overwrite is default

    Clue The question says the path already exists and no mode is set.

  • Domain 3

    Union aligns names

    Clue The column order differs between the two inputs.

  • Domain 4

    More nodes fix skew

    Clue Most tasks finish quickly and one dominates the stage time.

  • Domain 5

    Complete for everything

    Clue The query has no groupBy.

  • Domain 6

    Connect runs locally

    Clue The question asks where execution happens.

  • Domain 7

    Pandas-on-Spark is local

    Clue The question asks where the data lives.

Other Data & Analytics certifications

Study guides for exams that overlap with Spark Dev Assoc.

Practise what you read

Spark Dev Assoc domains: common questions

How many domains are in the Spark Dev Assoc exam?

Spark Dev Assoc has 7 domains with 32 official objectives between them: Apache Spark Architecture and Components, Using Spark SQL, Developing Apache Spark DataFrame/DataSet API Applications, Troubleshooting and Tuning Apache Spark DataFrame API Applications, Structured Streaming, Using Spark Connect to Deploy Applications and Using Pandas API on Spark.

Which Spark Dev Assoc domain carries the most weight?

Developing Apache Spark DataFrame/DataSet API Applications is the heaviest at 30%, followed by Apache Spark Architecture and Components (20%). The lightest is Using Pandas API on Spark (5%). Weights come from Databricks's official outline.

What is the Spark Dev Assoc exam format?

Questions: 45 scored multiple-choice questions (plus up to 10 unscored items). Time allowed: 90 minutes. Passing score: Not published by Databricks.

How long should I study for Spark Dev Assoc?

Our planning estimate is 61–85 hrs across all domains, split by weight. It is an editorial estimate, not an official figure, so adjust it to your experience and to how you score on practice questions.

Where should I start?

Start with Developing Apache Spark DataFrame/DataSet API Applications, because it carries the most marks, then work down by weight. Use the checklist on each domain page to confirm you can do what the domain asks before moving on.

Which outline is this guide based on?

This guide follows Databricks Certified Associate Developer for Apache Spark exam guide, August 2026 edition (exam published and guide updated 8 Oct 2026; current), published by Databricks. It was last reviewed in October 2026.

Domain names, weights and objectives come from Databricks's official outline (Databricks Certified Associate Developer for Apache Spark exam guide, August 2026 edition (exam published and guide updated 8 Oct 2026; current)). Question counts and study hours are editorial planning estimates, not official figures. Last reviewed October 2026.