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DATABRICKS

Databricks Certified Associate Developer for Apache Spark

ASSOCIATE-DEVELOPER-APACHE-SPARKApache Spark Developer Associate

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

573 practice questions · Updated 2026-07-30

7Domains
27Objectives
168Concepts
573Questions

ASSOCIATE-DEVELOPER-APACHE-SPARK Curriculum

Every domain, objective, and concept the ASSOCIATE-DEVELOPER-APACHE-SPARK exam measures.

Identify the advantages and challenges of implementing Spark.

2 concepts · 20 questions
  1. Advantages of Spark
  2. Challenges of Spark

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

6 concepts · 24 questions
  1. Spark Architecture Overview
  2. DataFrame and Dataset Concepts
  3. SparkSession Lifecycle
  4. Caching and Persistence
  5. Storage Levels
  6. Garbage Collection in Spark

Explain the Apache Spark™ Architecture execution hierarchy..

7 concepts · 27 questions
  1. Execution Hierarchy Overview
  2. Jobs
  3. Stages
  4. Tasks
  5. Dependencies and Shuffles
  6. Action-Triggered Execution
  7. Scheduling and Execution Flow

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

8 concepts · 29 questions
  1. Partitioning Fundamentals
  2. Partition Configuration
  3. Shuffle Operations
  4. Shuffle Partition Tuning
  5. Partitioning Strategies
  6. Partition Skew Handling
  7. Partitioning for Joins
  8. Data Locality and Partitioning

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

5 concepts · 19 questions
  1. Actions vs. Transformations
  2. Lazy Evaluation
  3. Lineage and DAG
  4. Execution Triggered by Actions
  5. Evaluation Order and Optimization

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

6 concepts · 25 questions
  1. Apache Spark Core
  2. Spark SQL
  3. DataFrames
  4. Pandas API on Spark
  5. Structured Streaming
  6. MLlib

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.

7 concepts · 20 questions
  1. Reading from JDBC sources
  2. Writing to JDBC sources
  3. Reading from file sources
  4. Writing to file sources
  5. Overwriting data
  6. Partitioning by column
  7. Efficient data reading and writing

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.

7 concepts · 19 questions
  1. SQL on Files Overview
  2. Querying ORC Files
  3. Querying JSON Files
  4. Querying CSV Files
  5. Querying Text Files
  6. Querying Delta Files
  7. Save Modes in Spark SQL

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

7 concepts · 22 questions
  1. Persistent Tables
  2. Saving DataFrames as Tables
  3. Managed vs External Tables
  4. Partitioning Tables
  5. Sorting Data in Tables
  6. Bucketing Tables
  7. Optimizing Data Retrieval

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

5 concepts · 12 questions
  1. DataFrame to Temporary View Registration
  2. Temporary View Scope and Lifetime
  3. Querying Temporary Views with SQL
  4. Overwriting and Replacing Temporary Views
  5. Temporary vs Global Temporary Views

DataFrame Transformations

14 concepts · 27 questions
  1. Adding and Dropping Columns
  2. Renaming Columns
  3. Filtering Rows
  4. Splitting Columns
  5. Exploding Arrays
  6. Deduplicating DataFrames
  7. Validating DataFrames
  8. Unix Epoch to Date String
  9. Extracting Date Components
  10. Sorting DataFrames
  11. Iterating Over DataFrames
  12. Printing Schema
  13. Converting DataFrame to Sequence/List
  14. Converting Sequence/List to DataFrame

Data Aggregation and Joins

11 concepts · 26 questions
  1. count
  2. approx_count_distinct
  3. mean
  4. summary
  5. inner_join
  6. left_join
  7. broadcast_join
  8. multiple_keys_join
  9. cross_join
  10. union
  11. union_all

Input/Output Operations

3 concepts · 9 questions
  1. Reading DataFrames with Schemas
  2. Writing DataFrames to Storage
  3. Overwriting DataFrames

User-Defined Functions and Stateful Operations

6 concepts · 26 questions
  1. User-Defined Functions (UDFs)
  2. UDF Performance and Null Handling
  3. Stateful Operations with mapGroupsWithState
  4. Stateful Operations with flatMapGroupsWithState
  5. StateStore and State Management
  6. State Timeouts and Cleanup

Shared Variables and Broadcast Joins

7 concepts · 21 questions
  1. Types of shared variables
  2. Broadcast variables purpose
  3. Broadcast variables implementation
  4. Accumulators purpose
  5. Accumulators implementation
  6. Broadcast joins purpose
  7. Broadcast joins implementation

Implement performance tuning strategies & optimize cluster utilization, including partitioning, repartitioning, coalescing, identifying data skew, and reducing shuffling

7 concepts · 29 questions
  1. Partitioning in Spark
  2. Repartitioning
  3. Coalescing
  4. Identifying Data Skew
  5. Mitigating Data Skew
  6. Reducing Shuffling
  7. Optimizing Cluster Utilization

Describe Adaptive Query Execution (AQE) and its benefits.

8 concepts · 26 questions
  1. Define Adaptive Query Execution
  2. Identify AQE benefits
  3. Describe AQE features
  4. Explain dynamic partition pruning
  5. Explain dynamic join strategy selection
  6. Explain dynamic skew join optimization
  7. Enable and configure AQE
  8. Monitor AQE behavior

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.

10 concepts · 35 questions
  1. Spark logging architecture
  2. Configuring Spark logging
  3. Publishing logs to external systems
  4. Customizing log formats
  5. Analyzing driver logs
  6. Analyzing executor logs
  7. Diagnosing out-of-memory errors
  8. Diagnosing cluster underutilization
  9. Using Spark UI for log correlation
  10. Monitoring application progress

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

5 concepts · 21 questions
  1. Structured Streaming overview
  2. Programming model
  3. Micro-batch processing
  4. Exactly-once semantics
  5. Fault tolerance mechanisms

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

4 concepts · 13 questions
  1. Streaming DataFrame and Dataset creation
  2. Output modes
  3. Output sinks
  4. Writing streaming queries

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

7 concepts · 15 questions
  1. Selecting columns from Streaming DataFrames
  2. Projecting and transforming streaming data
  3. Filtering streaming data
  4. Defining event-time windows
  5. Performing aggregations on streaming data
  6. Grouping streaming data with windows
  7. Handling late data and watermarks

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

6 concepts · 24 questions
  1. Deduplication without watermark
  2. Deduplication with watermark
  3. Watermark configuration
  4. State cleanup and memory management
  5. Event-time vs processing-time
  6. Impact of late data

Describe the features of Spark Connect.

4 concepts · 19 questions
  1. Spark Connect Overview
  2. Architecture of Spark Connect
  3. Benefits of Spark Connect
  4. Spark Connect API and Usage

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

5 concepts · 16 questions
  1. Deployment mode types
  2. Client mode
  3. Cluster mode
  4. Local mode
  5. Comparing deployment modes

Explain the advantages of using Pandas API on Spark.

1 concepts · 9 questions
  1. Advantages of Pandas API on Spark

Create and invoke Pandas UDF.

4 concepts · 12 questions
  1. Pandas UDF definition
  2. Pandas UDF invocation
  3. Pandas UDF function types
  4. Pandas UDF performance considerations
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Practice Free Questions

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