Databricks Certified Associate Developer for Apache Spark Preparation Details
The Databricks Certified Associate Developer for Apache Spark exam validates your ability to apply the Spark DataFrame API to real-world data engineering tasks. This guide covers every domain in the official exam guide, including Spark architecture, Spark SQL, DataFrame operations, Structured Streaming, and Spark Connect. You can also explore more Databricks certification study guides on the Databricks category to keep building your skills.
Databricks Certified Associate Developer for Apache Spark Materials
| Coursera | Databricks Lakehouse Fundamentals |
| Udemy | Databricks Associate Developer for Apache Spark 4 |
| Whizlabs | Databricks Associate Developer for Apache Spark |
Section 1: Apache Spark Architecture and Components
Topics Covered
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.
Classic compute configuration best practices
Describe the architecture of Apache Spark™, including DataFrame and Dataset concepts, SparkSession lifecycle, caching, storage levels, and garbage collection.
Spark SQL, DataFrames and Datasets Guide
Explain the Apache Spark™ Architecture execution hierarchy..
Monitoring and Instrumentation
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.
Spark SQL, DataFrames and Datasets Guide
Structured Streaming Programming Guide
Machine Learning Library (MLlib) Guide
Section 2: Using Spark SQL
Topics Covered
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.
What is Delta Lake in Databricks?
Save data to persistent tables while applying sorting and partitioning to optimize data retrieval.
Tutorial: Create and manage Delta Lake tables
Register DataFrames as temporary views in Spark SQL, allowing them to be queried with SQL syntax.
Spark SQL, DataFrames and Datasets Guide
Section 3: Developing Apache Spark™ DataFrame/DataSet API Applications
Topics Covered
Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays.
Spark SQL, DataFrames and Datasets Guide
Perform data deduplication and validation operations on DataFrames.
Spark SQL, DataFrames and Datasets Guide
Perform aggregate operations on DataFrames such as count, approximate count distinct, and mean, summary.
Spark SQL, DataFrames and Datasets Guide
Manipulate and utilize Date data type, such as Unix epoch to date string, and extract date component.
Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, and union all.
Spark SQL, DataFrames and Datasets Guide
Manage input and output operations by writing, overwriting, and reading DataFrames with schemas.
Spark SQL, DataFrames and Datasets Guide
Perform operations on DataFrames such as sorting, iterating, printing schema, and conversion between DataFrame and sequence/list formats.
Spark SQL, DataFrames and Datasets Guide
Create and invoke user-defined functions with or without stateful operators, including StateStores.
User-defined scalar functions – Python
What are user-defined functions (UDFs)?
Build a custom stateful application
Structured Streaming Programming Guide
Describe different types of variables in Spark, including broadcast variables and accumulators.
Describe the purpose and implementation of broadcast joins
Section 4: Troubleshooting and Tuning Apache Spark DataFrame API Applications.
Topics Covered
Implement performance tuning strategies & optimize cluster utilization, including partitioning, repartitioning, coalescing, identifying data skew, and reducing shuffling
Classic compute configuration best practices
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.
Monitoring and Instrumentation
Classic compute configuration best practices
Section 5: Structured Streaming
Topics Covered
Explain the Structured Streaming engine in Spark, including its functions, programming model, micro-batch processing, exactly-once semantics, and fault tolerance mechanisms.
Structured Streaming Programming Guide
Create and write Streaming DataFrames and Streaming Datasets, including the basic output modes and output sinks.
Structured Streaming Programming Guide
Delta Lake table streaming reads and writes
Perform basic operations on Streaming DataFrames and Streaming Datasets, such as selection, projection, window and aggregation.
Structured Streaming Programming Guide
Perform Streaming Deduplication in Structured Streaming, both with and without watermark usage.
Structured Streaming Programming Guide
Section 6: Using Spark Connect to deploy applications
Topics Covered
Describe the features of Spark Connect.
Describe the different deployment mode types (Client, Cluster, Local) in the Apache Spark™ environment.
Section 7: Using Pandas API on Spark
Topics Covered
Explain the advantages of using Pandas API on Spark.
Quickstart: Pandas API on Spark
Create and invoke Pandas UDF.
PySpark Usage Guide for Pandas with Apache Arrow
Wrapping Up Databricks Certified Associate Developer for Apache Spark
This guide covered the Databricks Certified Associate Developer for Apache Spark exam objectives in full, from Spark architecture and Spark SQL to Structured Streaming, Spark Connect, and the Pandas API on Spark. Work through the official documentation links under each objective and practice in Python to build real exam confidence. You can also explore more Databricks certification study guides on the Databricks category to keep building your skills. Have a question or tip? Leave a comment below.
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