A curated collection of demos, blog posts, official documentation, and training resources mapped to each exam objective for the Databricks Certified Data Engineer Associate certification (July 2025 version).
How to Use This Guide
For each exam section and objective, this guide provides:
- 📚 Official Documentation: Direct links to official Databricks docs (docs.databricks.com)
- 🎯 Demos: Interactive demonstrations and tutorials
- ✍️ Blog Posts: Technical articles and best practices
- 🎓 Training Resources: Courses, certifications, and learning materials
Resources are ranked by relevance score based on keyword matching. Review multiple resources for each objective to get comprehensive coverage.
About the Author
I’m a Databricks Solutions Architect Champion with extensive experience in data engineering and lakehouse architecture. This guide is designed to help you navigate the Data Engineer Associate certification, which focuses on building production-grade data pipelines using Databricks.
The Data Engineer Associate exam tests your practical knowledge of building, deploying, and managing data pipelines. This covers everything from Auto Loader and Lakeflow Declarative Pipelines to Unity Catalog governance and Databricks Asset Bundles.
I created this guide by analyzing the exam objectives and mapping them to the best available resources. My advice: get hands-on! Build pipelines with Auto Loader, create DLT workflows, deploy with DABs, and practice Unity Catalog permissions.
Find out what works best for you. I have previously written on my approach to taking certs here and have guides on many of the other Databricks Certifications as well. Good luck on your Databricks certification journey!
📖 Background Reading
Before diving into the objectives, these resources provide essential foundational context for the Data Engineer Associate exam:
Databricks Platform Fundamentals
What is a Data Lakehouse? – Read the Blog Post Start here! This foundational post explains the lakehouse architecture that underpins everything in Databricks. Understanding this concept is essential for the platform overview questions.
The Medallion Architecture – Read the Documentation The Bronze/Silver/Gold layering pattern is tested directly in Section 3. Make sure you understand when to use each layer.
Auto Loader & Data Ingestion
Auto Loader Overview – Read the Blog Post Covers the fundamentals of Auto Loader and why it’s preferred over traditional file ingestion patterns.
Schema Evolution with Auto Loader – Read the Documentation
Understanding cloudFiles.schemaEvolutionMode and rescue data columns is commonly tested.
Lakeflow Declarative Pipelines (Delta Live Tables)
Getting Started with Lakeflow Declarative Pipelines – Read the Documentation Core documentation for understanding streaming tables vs materialized views – a key exam topic.
Unity Catalog & Governance
Unity Catalog Best Practices – Read the Blog Post Section 5 (Governance) accounts for ~35% of the exam. This post covers permission models and governance patterns.
Delta Sharing Overview – Read the Blog Post Understand the difference between Databricks-to-Databricks sharing and open sharing protocols.
Databricks Asset Bundles
Introduction to DABs – Read the Documentation DABs are tested in Section 4. Understand the project structure and how bundles differ from traditional deployment.
Free Resources
Databricks Lakehouse Fundamentals – Free Course Free accredited learning path that covers many exam topics. I think this is worth the 2ish hours it takes to do. It will give you great context before diving into the associate level topics. I find courses like this will enable you to place the value of what you are learning with a wider group.
Databricks Free Edition – Sign Up Get hands-on practice with a free Databricks workspace – no credit card required.
📊 Exam Breakdown & Study Strategy
Exam Weight by Section
Understanding how the exam is weighted helps you prioritize your study time:
| Section | Exam Weight | Study Priority |
|---|---|---|
| Section 5: Data Governance & Quality | ~35% | 🔴 Critical |
| Section 3: Data Processing & Transformations | ~21% | 🔴 Critical |
| Section 2: Development and Ingestion | ~17% | 🟡 High |
| Section 4: Productionizing Data Pipelines | ~17% | 🟡 High |
| Section 1: Databricks Intelligence Platform | ~10% | 🟢 Medium |
🎯 How to Use This Guide Effectively
I’ve organized resources into four categories for each exam objective. Here’s how I recommend using them:
📚 Official Documentation (docs.databricks.com)
This is where you get the “official” definition and syntax. I use docs as my reference material when I need precise technical details.
My approach:
- Start with the “Getting Started” and “How-to” sections
- Bookmark key pages for quick review before the exam
- Don’t try to read every doc page – use them as reference material when you need specifics
Best for: Understanding exact syntax, parameters, and technical specifications
🎯 Interactive Demos (databricks.com/resources/demos)
Demos are where things click for me. Watching someone navigate the UI helps me understand workflows much faster than reading about them.
How I use demos:
- Before watching: I read the exam objective so I know what to focus on
- During the demo: I take screenshots of important configuration screens
- After the demo: I try to recreate what I saw in my own workspace – this is key!
Demo types:
- Hands-On Tutorials: Step-by-step guides (follow along in your workspace)
- Product Tours: Quick 3-5 minute overviews (watch these first)
- Video Demos: In-depth demonstrations (take notes, then practice)
Best for: Understanding UI workflows and seeing features in action
🎓 Training Resources (Databricks Academy)
If you prefer structured learning paths, these are great resources.
Training Courses (databricks.com/training):
- The official “Data Engineering with Databricks” course is excellent
- Many self-paced courses are free via Databricks Academy
- Hands-on labs are included – make sure you actually do them!
Best for: Structured learning and understanding how products fit together
My Recommended Study Path
Week 1-2: Foundation & Ingestion
- Start with platform understanding – compute types and use cases
- Master Auto Loader – syntax, sources, schema evolution
- Learn Medallion Architecture patterns
Week 3-4: Transformations & Pipelines
- Practice with Lakeflow Declarative Pipelines (DLT)
- Learn PySpark DataFrame operations
- Understand DDL/DML patterns in Databricks SQL
Week 5: Production & Governance
- Study Databricks Asset Bundles (DABs)
- Master Unity Catalog – permissions, roles, lineage
- Learn Delta Sharing and Lakehouse Federation
Week 6: Review & Practice
- Practice with Spark UI for optimization
- Review Workflow repair/rerun patterns
- Take practice exams
Practice & Validation
Hands-On Practice (This is critical!):
- Sign up for Databricks Free Edition (completely free, no credit card required)
- Don’t just read – actually practice the workflows shown in demos
- Build real pipelines with Auto Loader and DLT
- Set up Unity Catalog permissions and test Delta Sharing
- I can’t emphasize this enough: hands-on practice is the difference between passing and truly understanding the platform
Section 1: Databricks Intelligence Platform
Section Overview: 3 objectives
Recommended Demos for This Section
Start with these demos to get hands-on experience:
🎓 Hands-On Tutorials (Follow along in your workspace):
🎥 Product Tours (Quick 3-5 minute overviews):
📹 Video Demos (In-depth demonstrations):
1.1 Data Layout Optimization (Liquid Clustering & Predictive Optimization)
Objective: Enable features that simplify data layout decisions and optimize query performance.
📚 Official Documentation:
🎓 Training Resources:
1.2 Data Intelligence Platform Value Proposition
Objective: Explain the value of the Data Intelligence Platform.
📚 Official Documentation:
Top Demos:
1.3 Compute Selection for Use Cases
Objective: Identify the applicable compute to use for a specific use case.
📚 Official Documentation:
Top Demos:
Section 2: Development and Ingestion
Section Overview: 5 objectives
Recommended Demos for This Section
Start with these demos to get hands-on experience:
🎓 Hands-On Tutorials (Follow along in your workspace):
🎥 Product Tours (Quick 3-5 minute overviews):
📹 Video Demos (In-depth demonstrations):
2.1 Databricks Connect for Remote Development
Objective: Use Databricks Connect in a data engineering workflow.
📚 Official Documentation:
🎓 Training Resources:
2.2 Notebook Capabilities and Features
Objective: Determine the capabilities of the Notebooks functionality.
📚 Official Documentation:
Top Demos:
2.3 Auto Loader Sources and Use Cases
Objective: Classify valid Auto Loader sources and use cases.
📚 Official Documentation:
Top Demos:
- Spark Streaming Advanced
- Streaming Data With Delta Live Tables And Databricks Workflows
- Databricks Autoloader
2.4 Auto Loader Syntax and Configuration
Objective: Demonstrate knowledge of Auto Loader syntax.
📚 Official Documentation:
Top Demos:
2.5 Debugging and Troubleshooting Tools
Objective: Use Databricks’ built-in debugging tools to troubleshoot a given issue.
📚 Official Documentation:
Section 3: Data Processing & Transformations
Section Overview: 6 objectives
Recommended Demos for This Section
Start with these demos to get hands-on experience:
🎓 Hands-On Tutorials (Follow along in your workspace):
🎥 Product Tours (Quick 3-5 minute overviews):
📹 Video Demos (In-depth demonstrations):
3.1 Medallion Architecture Layers
Objective: Describe the three layers of the Medallion Architecture and explain the purpose of each layer in a data processing pipeline.
📚 Official Documentation:
Top Demos:
- Intro To Databricks Lakehouse Platform Architecture And Security
- Security Reference Architecture Aws
🎓 Training Resources:
- Databricks Streaming and Lakeflow Spark Declarative Pipelines
- Apache Spark Programming with Databricks
3.2 Cluster Configuration for Performance
Objective: Classify the type of cluster and configuration for optimal performance based on the scenario in which the cluster is used.
📚 Official Documentation:
Top Demos:
3.3 Lakeflow Declarative Pipelines Advantages
Objective: Emphasize the advantages of Lakeflow Spark Declarative Pipelines (for ETL process in Databricks).
📚 Official Documentation:
Top Demos:
3.4 Implementing Lakeflow Declarative Pipelines
Objective: Implement data pipelines using Lakeflow Spark Declarative Pipelines.
📚 Official Documentation:
Top Demos:
- Lakeflow Declarative Pipeline
- Lakeflow Declarative Pipelines
- Create Cluster Policy To Restrict Users
3.5 DDL and DML Operations
Objective: Identify DDL (Data Definition Language)/DML features.
📚 Official Documentation:
3.6 PySpark DataFrame Aggregations
Objective: Compute complex aggregations and Metrics with PySpark Dataframes.
📚 Official Documentation:
Section 4: Productionizing Data Pipelines
Section Overview: 5 objectives
Recommended Demos for This Section
Start with these demos to get hands-on experience:
🎥 Product Tours (Quick 3-5 minute overviews):
📹 Video Demos (In-depth demonstrations):
Also explore this Optimization Module on my Databricks navigator site
4.1 DABs vs Traditional Deployment
Objective: Identify the difference between DAB and traditional deployment methods.
📚 Official Documentation:
Top Demos:
🎓 Training Resources:
4.2 Asset Bundle Structure
Objective: Identify the structure of Asset Bundles.
📚 Official Documentation:
Top Demos:
4.3 Workflow Deployment and Repair
Objective: Deploy a workflow, repair, and rerun a task in case of failure.
📚 Official Documentation:
Top Demos:
4.4 Serverless Compute
Objective: Use serverless for a hands-off, auto-optimized compute managed by Databricks.
📚 Official Documentation:
Top Demos:
4.5 Spark UI Query Optimization
Objective: Analyzing the Spark UI to optimize the query.
📚 Official Documentation:
Section 5: Data Governance & Quality
Section Overview: 10 objectives
Recommended Demos for This Section
Start with these demos to get hands-on experience:
🎓 Hands-On Tutorials (Follow along in your workspace):
🎥 Product Tours (Quick 3-5 minute overviews):
📹 Video Demos (In-depth demonstrations):
5.1 Managed vs External Tables
Objective: Explain the difference between managed and external tables.
📚 Official Documentation:
Top Demos:
🎓 Training Resources:
5.2 Unity Catalog Permissions
Objective: Identify the grant of permissions to users and groups within UC.
📚 Official Documentation:
Top Demos:
5.3 Unity Catalog Roles
Objective: Identify key roles in UC.
📚 Official Documentation:
5.4 Audit Logs and System Tables
Objective: Identify how audit logs are stored.
📚 Official Documentation:
Top Demos:
5.5 Data Lineage in Unity Catalog
Objective: Use lineage features in Unity Catalog.
📚 Official Documentation:
Top Demos:
- Automated Data Lineage With Unity Catalog
- Data Lineage With Unity Catalog
- Data Lineage With Unity Catalog
5.6 Delta Sharing with Unity Catalog
Objective: Use the Delta Sharing feature available with Unity Catalog to share data.
📚 Official Documentation:
Top Demos:
5.7 Delta Sharing Advantages and Limitations
Objective: Identify the advantages and limitations of Delta sharing.
📚 Official Documentation:
Top Demos:
5.8 Delta Sharing Types
Objective: Identify the types of delta sharing: Databricks vs. external systems.
📚 Official Documentation:
Top Demos:
5.9 Cross-Cloud Data Sharing Costs
Objective: Analyze the cost considerations of data sharing across clouds.
📚 Official Documentation:
5.10 Lakehouse Federation Use Cases
Objective: Identify Use cases of Lakehouse Federation when connected to external sources.
📚 Official Documentation:
Top Demos:
Quick Reference Table
| Objective | Description | Demo Count |
|---|---|---|
| 1.1 | Enable features that simplify data layout decisions and opti… | 0 |
| 1.2 | Explain the value of the Data Intelligence Platform. | 35 |
| 1.3 | Identify the applicable compute to use for a specific use ca… | 3 |
| 2.1 | Use Databricks Connect in a data engineering workflow. | 0 |
| 2.2 | Determine the capabilities of the Notebooks functionality. | 2 |
| 2.3 | Classify valid Auto Loader sources and use cases. | 5 |
| 2.4 | Demonstrate knowledge of Auto Loader syntax. | 1 |
| 2.5 | Use Databricks’ built-in debugging tools to troubleshoot a g… | 0 |
| 3.1 | Describe the three layers of the Medallion Architecture and … | 2 |
| 3.2 | Classify the type of cluster and configuration for optimal p… | 1 |
| 3.3 | Emphasize the advantages of Lakeflow Spark Declarative Pipel… | 12 |
| 3.4 | Implement data pipelines using Lakeflow Spark Declarative Pi… | 12 |
| 3.5 | Identify DDL (Data Definition Language)/DML features. | 0 |
| 3.6 | Compute complex aggregations and Metrics with PySpark Datafr… | 0 |
| 4.1 | Identify the difference between DAB and traditional deployme… | 1 |
| 4.2 | Identify the structure of Asset Bundles. | 157 |
| 4.3 | Deploy a workflow, repair, and rerun a task in case of failu… | 1 |
| 4.4 | Use serverless for a hands-off, auto-optimized compute manag… | 2 |
| 4.5 | Analyzing the Spark UI to optimize the query. | 0 |
| 5.1 | Explain the difference between managed and external tables. | 16 |
| 5.2 | Identify the grant of permissions to users and groups within… | 15 |
| 5.3 | Identify key roles in UC. | 0 |
| 5.4 | Identify how audit logs are stored. | 5 |
| 5.5 | Use lineage features in Unity Catalog. | 4 |
| 5.6 | Use the Delta Sharing feature available with Unity Catalog t… | 7 |
| 5.7 | Identify the advantages and limitations of Delta sharing. | 6 |
| 5.8 | Identify the types of delta sharing: Databricks vs. external… | 8 |
| 5.9 | Analyze the cost considerations of data sharing across cloud… | 1 |
| 5.10 | Identify Use cases of Lakehouse Federation when connected to… | 1 |
Study Resources
Official Training
- Data Engineering with Databricks (Databricks Academy)
- Advanced Data Engineering with Databricks (Databricks Academy)
Certification Information
- Data Engineer Associate Exam Page
- Databricks Free Edition – Practice for free
Key Documentation
- Auto Loader – File ingestion
- Lakeflow Declarative Pipelines – ETL pipelines
- Databricks Asset Bundles – CI/CD
- Unity Catalog – Governance
- Delta Sharing – Data sharing
Last Updated: January 25, 2026 Exam Version: July 2025