Databricks Certified Associate Developer for Apache Spark

The Databricks Certified Associate Developer for Apache Spark validates foundational knowledge of Apache Spark architecture and proficiency with the Spark DataFrame API for data manipulation. It assesses the ability to perform column and row operations, handle missing data, join and partition DataFrames, write UDFs, and apply Spark SQL functions — as well as understanding core Spark concepts such as lazy evaluation, fault tolerance, shuffling, Structured Streaming, and Spark Connect. All exam code is in Python.

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What is the Databricks Certified Associate Developer for Apache Spark?

Who Should Take This Course?

Python developers building big data applications with Apache Spark
Data Engineers processing large-scale datasets with PySpark
Data Scientists using Spark for distributed data transformation
Software Engineers migrating ETL workloads to Spark-based platforms
Analytics Engineers working with Spark SQL and DataFrames
Professionals seeking a recognized Spark programming credential

What You Will Learn in the ASD Course

A comprehensive curriculum covering all exam objectives with hands-on labs and real-world practice.

Apache Spark Architecture and Components

Understand how Spark processes data at scale across a distributed cluster.

Spark execution modes: client, cluster, and local
Execution hierarchy: Jobs, Stages, Tasks, and Partitions
Lazy evaluation and the Spark DAG execution model
Fault tolerance, lineage, and RDD recovery mechanisms

Spark DataFrame API Applications

Manipulate data using the PySpark DataFrame API for real-world tasks.

Selecting, renaming, and casting columns
Filtering rows, sorting, and aggregating data
Handling missing data: dropna, fillna, and replace
Reading and writing DataFrames with schema management

Using Spark SQL

Query and transform data using Spark's SQL interface.

Registering DataFrames as temporary views and tables
Writing SQL queries against Spark DataFrames
Spark SQL built-in functions: string, date, and math
User-Defined Functions (UDFs) and their performance trade-offs

Joins, Partitioning, and Performance Tuning

Optimize Spark applications for performance and scalability.

Join types: inner, outer, left, right, cross, and semi-joins
Shuffling and broadcast joins for performance optimization
Partitioning DataFrames and coalesce vs. repartition
Caching and persistence strategies for iterative workloads

Structured Streaming and Spark Connect

Process real-time data streams and build remote Spark applications.

Structured Streaming: sources, sinks, and watermarks
Streaming aggregations and windowing operations
Spark Connect: decoupled client-server Spark architecture
Pandas API on Spark for familiar DataFrame syntax at scale

Course Prerequisites

Pre-requisites training is free when you purchase the course from ProSupport

Intermediate Python programming skills (PySpark code used throughout)
Basic understanding of data structures and distributed computing concepts
Familiarity with SQL for data querying
6+ months of hands-on experience with Spark (recommended)
No formal prerequisites — Databricks Academy Spark training highly recommended

Exam Information

Everything you need to know about the ASD certification exam.

Exam Component	Details
Exam Name	Databricks Certified Associate Developer for Apache Spark
Exam Code	ASD
Exam Type	Multiple Choice
Total Questions	45
Passing Score	70%
Exam Duration	90 minutes
Language	English
Exam Provider	Databricks / Kryterion (online proctored or test center)
Exam Focus	Spark architecture, PySpark DataFrame API, Spark SQL, UDFs, Structured Streaming, and performance tuning
Exam Registration	Databricks Academy portal (academy.databricks.com)
Retake Policy	14-day waiting period before retake
Certification Validity	2 years