DATABRICKS

Databricks Certified Associate Developer for Apache Spark

ASSOCIATE-DEVELOPER-APACHE-SPARK

The Databricks Certified Associate Developer for Apache Spark certification validates your ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session. It is designed for data practitioners who work with Python and want to demonstrate a solid understanding of Spark architecture, components, and common troubleshooting techniques. Earning this credential signals that you can handle real-world Spark development tasks with confidence.

Practice Free Questions

7Domains

27Objectives

125Practice pages

573Free questions

ASSOCIATE-DEVELOPER-APACHE-SPARK free practice questions

Choose any objective and open any question page — no sign-in required.

Identify the advantages and challenges of implementing Spark.

20 questions · 4 pages

Questions 1–5 6–10 11–15 16–20

Identify the role of core components of Apache Spark™'s Architecture, including cluster, driver node, worker nodes/executors, CPU cores, and memory.

28 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–28

Describe the architecture of Apache Spark™, including DataFrame and Dataset concepts, SparkSession lifecycle, caching, storage levels, and garbage collection.

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Explain the Apache Spark™ Architecture execution hierarchy..

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Configure Spark partitioning in distributed data processing, including shuffles and partitions

29 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–29

Describe the execution patterns of the Apache Spark™ engine, including actions, transformations, and lazy evaluation.

19 questions · 4 pages

Questions 1–5 6–10 11–15 16–19

Identify the features of the Apache Spark Modules, including Core, Spark SQL, DataFrames, Pandas API on Spark, Structured Streaming, and MLib.

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

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.

20 questions · 4 pages

Questions 1–5 6–10 11–15 16–20

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.

19 questions · 4 pages

Questions 1–5 6–10 11–15 16–19

Save data to persistent tables while applying sorting and partitioning to optimize data retrieval.

22 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–22

Register DataFrames as temporary views in Spark SQL, allowing them to be queried with SQL syntax.

12 questions · 3 pages

Questions 1–5 6–10 11–12

DataFrame Transformations

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Data Aggregation and Joins

26 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–26

Input/Output Operations

9 questions · 2 pages

Questions 1–5 6–9

User-Defined Functions and Stateful Operations

26 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–26

Shared Variables and Broadcast Joins

21 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–21

Explain the Structured Streaming engine in Spark, including its functions, programming model, micro-batch processing, exactly-once semantics, and fault tolerance mechanisms.

21 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–21

Create and write Streaming DataFrames and Streaming Datasets, including the basic output modes and output sinks.

13 questions · 3 pages

Questions 1–5 6–10 11–13

Perform basic operations on Streaming DataFrames and Streaming Datasets, such as selection, projection, window and aggregation.

15 questions · 3 pages

Questions 1–5 6–10 11–15

Perform Streaming Deduplication in Structured Streaming, both with and without watermark usage.

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Describe the features of Spark Connect.

19 questions · 4 pages

Questions 1–5 6–10 11–15 16–19

Describe the different deployment mode types (Client, Cluster, Local) in the Apache Spark™ environment.

16 questions · 4 pages

Questions 1–5 6–10 11–15 16–16

Explain the advantages of using Pandas API on Spark.

9 questions · 2 pages

Questions 1–5 6–9

Create and invoke Pandas UDF.

12 questions · 3 pages

Questions 1–5 6–10 11–12

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Percentages reflect share of the current practice bank, not official exam weightings. Every page above is a live, crawlable practice page (27 objectives · 125 pages).

Databricks Certified Associate Developer for Apache Spark

ASSOCIATE-DEVELOPER-APACHE-SPARK free practice questions

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..

Configure Spark partitioning in distributed data processing, including shuffles and partitions

Describe the execution patterns of the Apache Spark™ engine, including actions, transformations, and lazy evaluation.

Identify the features of the Apache Spark Modules, including Core, Spark SQL, DataFrames, Pandas API on Spark, Structured Streaming, and MLib.

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.

DataFrame Transformations

Data Aggregation and Joins

Input/Output Operations

User-Defined Functions and Stateful Operations

Shared Variables and Broadcast Joins

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.

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.

Describe the features of Spark Connect.

Describe the different deployment mode types (Client, Cluster, Local) in the Apache Spark™ environment.

Explain the advantages of using Pandas API on Spark.

Create and invoke Pandas UDF.

Databricks Certified Associate Developer for Apache Spark

ASSOCIATE-DEVELOPER-APACHE-SPARK free practice questions

Domain 1 · Apache Spark Architecture and Components30% of practice bank · 7 objectives · 172 questions30%

Domain 2 · Using Spark SQL13% of practice bank · 4 objectives · 73 questions13%

Domain 3 · Developing Apache Spark™ DataFrame/DataSet API Applications19% of practice bank · 5 objectives · 109 questions19%

Domain 4 · Troubleshooting and Tuning Apache Spark DataFrame API Applications.16% of practice bank · 3 objectives · 90 questions16%

Domain 5 · Structured Streaming13% of practice bank · 4 objectives · 73 questions13%

Domain 6 · Using Spark Connect to deploy applications6% of practice bank · 2 objectives · 35 questions6%

Domain 7 · Using Pandas API on Spark4% of practice bank · 2 objectives · 21 questions4%