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DATA-ENG-ASSOC Exam Topics and Domains
DATA-ENG-ASSOC is organized into 5 weighted domains. Expect to work with Delta Lake, Serverless Compute, Unity Catalog, Git Integration, and more.
Databricks Intelligence Platform
Platform Features and Optimization
- Enable features that simplify data layout decisions and optimize query performance
- Explain the value of the Data Intelligence Platform
- Identify the applicable compute to use for a specific use case
Development and Ingestion
Databricks Connect
Use Databricks Connect in a data engineering workflow
Notebooks Functionality
Determine the capabilities of Notebooks functionality
Auto Loader
- Classify valid Auto Loader sources and use cases
- Demonstrate knowledge of Auto Loader syntax
Debugging Tools
Use Databricks' built-in debugging tools to troubleshoot a given issue
Data Processing & Transformations
Medallion Architecture
Describe the three layers of the Medallion Architecture and explain the purpose of each layer in a data processing pipeline
Cluster Configuration
Classify the type of cluster and configuration for optimal performance based on the scenario in which the cluster is used
Lakehouse Declarative Pipelines (LDP/DLT)
- Emphasize the advantages of LDP (for ETL process in Databricks)
- Implement data pipelines using LDP
SQL Operations
Identify DDL (Data Definition Language)/DML features
PySpark DataFrames
Compute complex aggregations and Metrics with PySpark DataFrames
Productionizing Data Pipelines
Databricks Asset Bundles (DAB)
- Identify the difference between DAB and traditional deployment methods
- Identify the structure of Asset Bundles
Workflow Management
Deploy a workflow, repair, and rerun a task in case of failure
Serverless Computing
Use serverless for a hands-off, auto-optimized compute managed by Databricks
Performance Optimization
Analyzing the Spark UI to optimize the query
Data Governance & Quality
Table Management
Explain the difference between managed and external tables
Unity Catalog Security
- Identify the grant of permissions to users and groups within UC
- Identify key roles in UC
Audit and Lineage
- Identify how audit logs are stored
- Use lineage features in Unity Catalog
Delta Sharing
- Use the Delta Sharing feature available with Unity Catalog to share data
- Identify the advantages and limitations of Delta sharing
- Identify types of delta sharing- Databricks vs external system
- Analyze the cost considerations of data sharing across clouds
Lakehouse Federation
Identify Use cases of Lakehouse Federation when connected to external sources
How do I earn this certification?
Passing DATA-ENG-ASSOC earns the Databricks Certified Data Engineer Associate certification. It sits in the Data Engineering track.
- databricks-certified-machine-learning-associate - Machine Learning AssociateExpand into ML engineering
- databricks-certified-data-analyst-associate - Data Analyst AssociateFocus on analytics and SQL
- databricks-accredited-lakehouse-platform-architect - Lakehouse Platform Architect Advanced architecture focus
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
Use the official PlanetCert Practice Test alongside the study plan below to prepare efficiently for DATA-ENG-ASSOC.
What's changed on this exam?
- ACTIVE
- Last content update: 2025-07-25
- Announcement date: 2025-06-01
- Databricks Asset Bundles (DAB) GA New topic in Productionizing Data Pipelines domain • Release date: 2025-03-01
- Serverless Compute Enhanced GA Emphasized in compute selection questions • Release date: 2025-06-01
- Delta Sharing 3.0 Expanded coverage in governance domain • Release date: 2025-05-15
- Auto Loader Latest 30% of Development and Ingestion domain • Release date: Continuous updates
Who should take this exam?
This exam is typically taken by Entry-level data engineers and Data analysts transitioning to engineering.
- 6+ months of hands-on experience with Databricks
- Working knowledge of Python and SQL
- Experience with Apache Spark
- Understanding of data engineering concepts