A curated collection of demos, blog posts, official documentation, and training resources mapped to each exam objective for the Databricks Certified Data Analyst Associate certification (Oct 2025 version).
If you are renewing this exam check the What Changed in the 2025 Data Analyst Exam? (2023 vs 2025 Comparison) post
How to Use This Guide
For each exam section and objective, this guide provides:
- 📚 Official Documentation: Direct links to official Databricks docs
- 🎯 Demos: Interactive demonstrations and tutorials
- ✍️ Blog Posts: Technical articles and best practices
- 🎓 Training Resources: Courses, certifications, and learning materials
About the Author
I’m a Databricks Solutions Architect Champion, and I recently had the opportunity to beta test the new October 2025 Data Analyst exam. I’ve now passed both the 2023 version and the 2025 beta exam, and the certification reflects the evolution of the platform in this area.
The exam has shifted from being a “Databricks SQL specialist” certification to a comprehensive “Data Intelligence Platform analyst” certification. This isn’t just marketing speak – it reflects the real-world evolution of what data analysts do with Databricks today.
I created this guide by combining insights from both exam experiences with extensive hands-on practice in Databricks. Throughout my preparation, I found that nothing beats actually using the platform. So while this guide points you to documentation, demos, and training materials, my biggest advice is to get your hands dirty. Sign up for a workspace, build dashboards, create Genie spaces, and practice writing queries.
Find out what works best for you. Good luck on your Databricks certification journey!
📊 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 4: Executing Queries (SQL & Warehouses) | 20% | 🔴 Critical |
| Section 6: Dashboards and Visualizations | 16% | 🔴 Critical |
| Section 5: Analyzing Queries | 15% | 🟡 High |
| Section 7: AI/BI Genie Spaces | 12% | 🟡 High |
| Section 1: Platform Understanding | 11% | 🟡 High |
| Section 2: Managing Data | 8% | 🟢 Medium |
| Section 9: Securing Data | 8% | 🟢 Medium |
| Section 3: Importing Data | 5% | 🟢 Medium |
| Section 8: Data Modeling | 5% | 🟢 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, you’ll burn out. 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 and note any tips mentioned
- After the demo: I try to recreate what I saw in my own workspace – this is key!
Demo types:
- Video Tours: Quick 3-5 minute overviews (watch these first)
- Tutorials: Step-by-step guides (follow along in your workspace)
- Videos: In-depth demonstrations (take notes, then practice)
Best for: Understanding UI workflows and seeing features in action. Critical for exam questions about “using the UI to…”
✍️ Blog Posts (databricks.com/blog & community.databricks.com)
Blog posts give me the “why” and “when” that documentation sometimes misses. They’re written by people who’ve actually solved real problems.
How I use blogs:
- I read them after reviewing docs to get practical context
- I focus on “how-to” and “best practices” posts
- I look for common pitfalls and troubleshooting tips
- Community blogs often include exam-relevant scenarios
What blogs are good for:
- Understanding why certain features exist (helps with conceptual questions)
- Learning when to use one approach vs. another (decision-making questions)
- Seeing how others have solved real problems (scenario-based questions)
Best for: Use cases, decision criteria, and real-world best practices
🎓 Training Resources & Product Pages
If you prefer structured learning paths, these are great resources.
Training Courses (databricks.com/training):
- The official “Data Analysis 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!
Product Pages (databricks.com/product):
- Quick overview of what a product does at a high level
- Useful for understanding use cases and integration points
Discovery Pages (databricks.com/discover):
- Good concept explanations (like “What is Delta Lake?”)
- Architecture diagrams help visualize how things fit together
Best for: Structured learning and understanding how products fit into the bigger picture
My Recommended Study Path
Here’s how I’d approach preparing for this exam if I were starting from scratch:
Week 1-2: Foundation & High-Weight Topics
I always start with the big picture to understand how everything fits together.
Section 1 (Platform Understanding) – 11% of exam
- Start with the platform overview and Unity Catalog
- Watch the workspace creation and platform tour demos
- This gives you the foundation for everything else
Section 4 (Executing Queries) – 20% of exam
- Read SQL Warehouse docs, watch the demos
- Then actually write queries in the SQL Editor yourself
- Practice with different query types and patterns
Week 3-4: Dashboards & Analysis
Section 6 (Dashboards) – 16% of exam
- Watch ALL the dashboard demos – this is UI-heavy, you need to see it
- Then build a real dashboard yourself with parameters and sharing
Section 5 (Analyzing Queries) – 15% of exam
- Query Insights and Profiler demos are critical here
- Read the performance optimization blog posts
Week 5: New Features
- Section 7 (Genie Spaces) – 12% of exam
- This is completely new, so watch all the Genie demos
- Create your own Genie space and experiment with it
Week 6: Remaining Topics & Review
- Cover Sections 2, 3, 8, 9 (26% combined)
- Review the Quick Reference Tables in this guide
- Work through the sample questions in the official exam guide
Find What Works for You
Everyone learns differently. Here’s what I’ve seen work for different learning styles:
🎥 If you learn best by watching:
- Start with demos, then read docs for the details you need
- Dashboard and Genie sections are particularly UI-heavy
- I (sometimes) watch demos at 1.5x speed to save time
📖 If you prefer reading:
- Start with docs, then watch demos to see how it actually works
- Blog posts give you deeper context
- I take my own notes as I read – helps me remember
🛠️ If you need to do it to learn it (this is me!):
- Get a Databricks Free Edition workspace
- Follow the tutorial demos step-by-step in your own workspace
- Build real projects that combine multiple objectives
- Nothing beats hands-on practice
⏱️ If you’re short on time:
- Focus on Sections 4, 6, 5, 7 first (63% of the exam)
- Use the Quick Reference Table for rapid review
- Skim the “Getting Started” sections of docs instead of reading everything
Practice & Validation
Official Practice Questions:
- Review the 10 sample questions in the pdf in this page Databricks Exam Guide
- Each sample question maps to specific objectives in this guide
- Use the answers to identify knowledge gaps
Hands-On Practice (This is critical!):
- Sign up for Databricks Free Edition (completely free, no credit card required)
- Or request a trial workspace from your organization
- Don’t just read, actually practice the workflows shown in demos
- Build real dashboards and create Genie spaces yourself
- Write queries in the SQL Editor
- Set up alerts and refresh schedules
- I can’t emphasize this enough: hands-on practice is the difference between passing and truly understanding the platform
Knowledge Checks:
- Can you explain each objective in your own words?
- Can you navigate to each feature in the UI?
- Do you know WHEN to use each tool/feature?
- Can you differentiate between similar concepts (e.g., materialized view vs. streaming table)?
Things I Wish I’d Known Earlier
Here are mistakes I’ve seen (and made):
Don’t do this:
- Skip the demos thinking you’ll “just read the docs” (especially for dashboards and Genie)
- Try to memorize everything – the exam tests understanding, not memory
- Ignore the new features like Genie because they seem scary (they’re actually heavily tested)
- Only study documentation without actually trying things
Do this instead:
- Focus on understanding “why” and “when,” not just “what”
- Actually practice in a real Databricks workspace
- Learn the differences between similar features (like materialized view vs. streaming table)
- Skim recent release notes for updates to features
- Print the exam guide and tick off each point as you study to keep track of your learning
Exam Day
The night before:
- Review the Quick Reference Table at the end of this guide
- Skim your notes on the high-weight sections (4, 6, 5, 7)
- Look over the official sample questions one more time
- Get a good night’s sleep – seriously, don’t cram
During the exam:
- Read questions carefully (watch for words like “NOT” or “EXCEPT”)
- Eliminate the obviously wrong answers first
- Flag questions you’re unsure about and come back to them
- You have about 2 minutes per question (45 questions, 90 minutes) – manage your time
Section 1: Understanding Databricks Data Intelligence Platform
Section Overview: 11% of exam | 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 Core Components of the Databricks Intelligence Platform
Objective: Describe the core components of the Databricks Intelligence Platform, including Mosaic AI, DeltaLive tables, Lakeflow Jobs, Data Intelligence Engine, Delta Lake, Unity Catalog, and Databricks SQL.
📚 Official Documentation:
- Getting Started with Databricks
- Lakehouse Architecture
- Delta Live Tables
- Delta Lake
- Unity Catalog
- Databricks SQL
Top Demos:
Top Blog Posts:
Top Training Resources:
1.2 Unity Catalog Deep Dive
Objective: Understand catalogs, schemas, managed and external tables, access controls, views, certified tables, and lineage within the Catalog Explorer interface.
📚 Official Documentation:
- Database Objects in Databricks
- What are Catalogs?
- What are Schemas?
- Managed and External Tables
- Catalog Explorer
- Data Lineage
Top Demos:
Top Blog Posts:
Top Training Resources:
1.3 Databricks Marketplace
Objective: Describe the role and features of Databricks Marketplace.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
- Revolutionizing Data in Sports: Game-Changing Impact of Databricks Marketplace and Delta Sharing
- Introducing Databricks Marketplace
Top Resources:
Section 2: Managing Data
Section Overview: 8% of exam | 4 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 Unity Catalog for Data Management
Objective: Use Unity Catalog to discover, query, and manage certified datasets.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
- Explore Data Instantly with Databricks Assistant in Unity Catalog
- Scaling Machine Learning with Ray and Databricks: A Shutterfly Use Case
Top Resources:
2.2 Catalog Explorer: Tagging and Lineage
Objective: Use the Catalog Explorer to tag a data asset and view its lineage.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
- Enforce Consistent Secure Tagging Across Data and AI Assets: Governed Tags in Unity Catalog Public
- Accelerating Discovery: Unity Catalog Revamped Catalog Explorer
- Unity Catalog Governance: Action Monitoring, Reporting and Lineage
2.3 Data Cleaning in SQL
Objective: Perform data cleaning on Unity Catalog Tables in SQL, including removing invalid data or handling missing values.
📚 Official Documentation:
- SQL Functions Reference
- NULL Handling (COALESCE, IFNULL)
- String Functions
- Data Type Conversion (CAST, TRY_CAST)
Top Blog Posts:
- Normalised Ingestion of Vehicle Telematics
- Auto Loader: JSON Data Ingestion with Built-in Data Audit and Validation
Top Resource:
Section 3: Importing Data
Section Overview: 5% of exam | 2 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):
3.1 Data Ingestion Approaches
Objective: Explain the approaches for bringing data into Databricks, covering ingestion from S3, data sharing with external systems via Delta Sharing, API-driven data intake, the Auto Loader feature, and Marketplace.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
- Auto Loader: JSON Data Ingestion with Built-in Data Audit and Validation
- AutoLoader: XML Data Ingestion with Built-in Data Audit & Validation
- Ingesting Source Systems – Custom Data Source API JDBC Connections
Top Training Resources:
3.2 Upload Data via UI
Objective: Use the Databricks Workspace UI to upload a data file to the platform.
📚 Official Documentation:
Top Demos:
Top Training Resource:
Section 4: Executing Queries with Databricks SQL and SQL Warehouses
Section Overview: 20% of exam | 9 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):
- Databricks LakehouseIQ Databricks Assistant
- Introducing Databricks AI/BI Genie Enduser
- Query Federation Product Tour
- Unity Catalog Setup
- Introducing Unity Catalog
📹 Video Demos (In-depth demonstrations):
- Serverless SQL Warehouses
- Databricks SQL
- Lakehouse Federation
- Understanding Your Business with Unity Catalog Metric View
- Delta Live Tables Overview
- Delta Lake
4.1 Databricks Assistant for Query Writing
Objective: Utilize Databricks Assistant within a Notebook or SQL Editor to facilitate query writing and debugging.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
- Explore Data Instantly with Databricks Assistant in Unity Catalog
- Databricks Assistant Data Science Agent: Autonomous Analytics for Petroleum Engineers
- Introducing Databricks Assistant Data Science Agent
Top Resources:
4.2 SQL Warehouse Role in Query Execution
Objective: Explain the role a SQL Warehouse plays in query execution.
📚 Official Documentation:
Top Demos:
Top Resources:
4.3 Cross-System Analytics with Federated Data
Objective: Querying cross-system analytics by joining data from a Delta table and a federated data source.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
- Announcing General Availability: Lakehouse Federation
- Introducing Salesforce Connectors for Lakehouse Federation and Lakeflow Connect
Top Resources:
4.4 Materialized Views and Streaming Tables
Objective: Create a materialized view, including knowing when to use Streaming Tables and Materialized Views, and differentiate between dynamic and materialized views.
📚 Official Documentation:
Top Demos:
- Table ACL and Dynamic Views with UC
- Understanding Your Business with Unity Catalog Metric View
- Delta Live Tables Overview
Top Blog Posts:
- Announcing Public Preview: Streaming Table and Materialized View Sharing
- Now GA: Share Materialized Views and Streaming Tables with Delta Sharing
4.5 Aggregate Operations
Objective: Perform aggregate operations such as count, approximate count distinct, mean, and summary statistics.
📚 Official Documentation:
Top Blog Posts:
4.6 Join Operations and Set Operations
Objective: Write queries to combine tables using various join operations (inner, left, right, and so on) with single or multiple keys, as well as set operations like union and union all, including the differences between the joins (inner, left, right, and so on).
📚 Official Documentation:
Top Blog Posts:
4.7 Sorting and Filtering Operations
Objective: Perform sorting and filtering operations on a table.
📚 Official Documentation:
4.8 Creating Tables from Multiple Sources
Objective: Create managed tables and external tables, including creating tables by joining data from multiple sources (e.g., CSV, Parquet, Delta tables) to create unified datasets, including Unity Catalog.
📚 Official Documentation:
Top Demos:
Latest Release Note:
4.9 Delta Lake Time Travel
Objective: Use Delta Lake’s time travel to access and query historical data versions.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
Top Resources:
Section 5: Analyzing Queries
Section Overview: 15% of exam | 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):
5.1 Photon Features and Benefits
Objective: Understand the Features, Benefits, and Supported Workloads of Photon.
📚 Official Documentation:
Top Blog Posts:
Top Resource:
5.2 Identifying Poorly Performing Queries
Objective: Identify poorly performing queries in the Databricks Intelligence platform, such as Query Insights, Query Profiler log, etc.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
5.3 Delta Lake Audit and History
Objective: Utilize Delta Lake to audit and view history, validate results, and compare historical results or trends.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
- Auto Loader: JSON Data Ingestion with Built-in Data Audit and Validation
- AutoLoader: XML Data Ingestion with Built-in Data Audit & Validation
5.4 Query History and Caching
Objective: Utilize query history and caching to reduce development time and query latency.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
5.5 Liquid Clustering
Objective: Apply Liquid Clustering to improve query speed when filtering large tables on specific columns.
📚 Official Documentation:
Top Blog Posts:
Top Resource:
5.6 Query Debugging
Objective: Fix a query to achieve the desired results.
Top Demos:
Top Blog Posts:
Section 6: Working with Dashboards and Visualizations
Section Overview: 16% of exam | 6 objectives
Recommended Demos for This Section
Start with these demos to get hands-on experience:
🎓 Hands-On Tutorials (Follow along in your workspace):
- Dashboard Schedule and Subscriptions
- Dashboard Parameters Tutorial
- Query Parameters Tutorial
- Dashboard Sharing and Embedding
- Alerts with Databricks SQL
- Dashboard Alerts Tutorial
🎥 Product Tours (Quick 3-5 minute overviews):
📹 Video Demos (In-depth demonstrations):
6.1 Building AI/BI Dashboards
Objective: Build dashboards using AI/BI Dashboards, including multi-tabs/page layouts, multiple data sources/datasets, and widgets (visualizations, text, images).
📚 Official Documentation:
Top Demos:
Top Blog Posts:
Top Resources:
6.2 Creating Visualizations
Objective: Create visualizations in notebooks and the SQL editor.
Top Demos:
Top Blog Posts:
6.3 Working with Parameters
Objective: Work with parameters in SQL queries and dashboards, including defining, configuring, and testing parameters.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
6.4 Dashboard Permissions and Sharing
Objective: Configure permissions through the UI to share dashboards with workspace users/groups, external users through shareable links, and embed dashboards in external apps.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
6.5 Dashboard Scheduling
Objective: Schedule an automatic dashboard refresh.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
6.6 Configuring Alerts
Objective: Configure an alert with a desired threshold and destination.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
6.7 Effective Visualization Types
Objective: Identify the effective visualization type to communicate insights clearly.
📚 Official Documentation:
Top Demos:
Section 7: Developing, Sharing, and Maintaining AI/BI Genie Spaces
Section Overview: 12% of exam | 4 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):
7.1 AI/BI Genie Purpose and Features
Objective: Describe the purpose, key features, and components of AI/BI Genie spaces.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
Top Resources:
7.2 Creating Genie Spaces
Objective: Create Genie spaces by defining reasonable sample questions and domain-specific instructions, choosing SQL warehouses, curating Unity Catalog datasets (tables, views…), and vetting queries as Trusted Assets.
Top Demos:
Top Blog Posts:
7.3 Genie Permissions and Distribution
Objective: Assign permissions via the UI and distribute Genie spaces using embedded links and external app integrations.
Top Demos:
7.4 Optimizing Genie Spaces
Objective: Optimize AI/BI Genie spaces by tracking user questions, response accuracy, and feedback; updating instructions and trusted assets based on stakeholder input; validating accuracy with benchmarks; refreshing Unity Catalog metadata.
Top Blog Posts:
Section 8: Data Modeling with Databricks SQL
Section Overview: 5% of exam | 2 objectives
Recommended Demos for This Section
Start with these demos to get hands-on experience:
🎓 Hands-On Tutorials (Follow along in your workspace):
📹 Video Demos (In-depth demonstrations):
8.1 Data Modeling Techniques
Objective: Apply industry-standard data modeling techniques, such as star, snowflake, and data vault schemas, to analytical workloads.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
Top Resources:
8.2 Medallion Architecture
Objective: Understand how industry-standard models align with the Medallion Architecture.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
Section 9: Securing Data
Section Overview: 8% of exam | 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):
9.1 Unity Catalog Security
Objective: Use Unity Catalog roles and sharing settings to ensure workspace objects are secure.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
Top Resources:
9.2 Unity Catalog 3-Level Namespace
Objective: Understand how the 3-level namespace (Catalog / Schema / Tables or Volumes) works in the Unity Catalog.
📚 Official Documentation:
Top Demos:
Top Resources:
9.3 Data Security Best Practices
Objective: Apply best practices for storage and management to ensure data security, including table ownership and PII protection.
📚 Official Documentation:
Top Demos:
Top Blog Posts:
Top Resources:
Quick Reference Table
This table provides a quick overview of available resources for each exam objective. Click links to access the top resource in each category.
Section 1: Understanding Databricks Data Intelligence Platform
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| Core Components (Mosaic AI, DLT, Lakeflow, Delta Lake, Unity Catalog, SQL) | Getting Started with Databricks | Workspace Creation | What is a Data Lakehouse? | Databricks Lakehouse Fundamentals |
| Unity Catalog Deep Dive (Catalogs, schemas, tables, access controls, lineage) | Database Objects in Databricks | Introducing Unity Catalog | Catalog Explorer Revamp | Data Management with UC |
| Databricks Marketplace | What is Databricks Marketplace? | Managing Consumer Requests | Marketplace & Delta Sharing Impact | Databricks Marketplace |
Section 2: Managing Data
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| Unity Catalog for Data Management | Unity Catalog | Introducing Unity Catalog | Explore Data with Assistant | Data Management with UC |
| Catalog Explorer: Tagging & Lineage | Catalog Explorer | Data Lineage Tutorial | Governed Tags | Advantage Lakehouse |
| Data Cleaning in SQL | SQL Functions Reference | – | Normalised Ingestion | SQL Language Reference |
Section 3: Importing Data
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| Data Ingestion Methods (S3, Delta Sharing, API, Auto Loader, Marketplace) | Connect to Data | Auto Loader Demo | Auto Loader JSON Ingestion | Lakeflow Connect |
| Upload Data via UI | Upload Data | Upload Data UI Demo | – | – |
Section 4: Executing Queries with Databricks SQL
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| Databricks Assistant | Use Assistant | LakehouseIQ Assistant | Explore Data with Assistant | Databricks Assistant Product |
| SQL Warehouse | SQL Warehouse | Serverless SQL Warehouses | – | Data Warehouse |
| Federated Queries | Query Federation | Lakehouse Federation | Lakehouse Federation GA | Lakehouse Federation eBook |
| Materialized Views & Streaming Tables | Materialized Views | Dynamic Views with UC | Streaming Table & MV Sharing | Delta Live Tables |
| Aggregate Operations | SQL Functions | – | – | – |
| Join Operations & Set Operations | JOIN Operations | – | – | – |
| Sorting & Filtering | SELECT Statement | – | – | – |
| Creating Tables (Managed/External, Multiple Sources) | CREATE TABLE | Unity Catalog Setup | – | Unity Catalog Training |
| Delta Lake Time Travel | Time Travel | Delta Lake Demo | Delta Lake Time Travel | Delta Lake |
Section 5: Analyzing Queries
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| Photon Features & Benefits | Photon | – | Photon-Native Extensions | Photon Product |
| Query Performance (Query Insights, Query Profiler) | Query Profile | Query Insights Demo | Query Insights GA | Performance Best Practices |
| Delta Lake Audit & History | Delta History | Audit Logs | Auto Loader with Audit | Delta Lake |
| Query History & Caching | Disk Cache | Query Results Cache | Disk Cache Acceleration | Performance Efficiency |
| Liquid Clustering | Liquid Clustering | – | Liquid Clustering GA | Liquid Clustering |
| Query Debugging | SQL Reference | LakehouseIQ Assistant | SQL CTEs Pitfalls | Databricks Assistant |
Section 6: Dashboards and Visualizations
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| AI/BI Dashboards | AI/BI Dashboards | AI/BI Dashboard Demo | AI/BI Dashboards Launch | AI/BI Dashboards Product |
| Creating Visualizations | Dashboards | Creating Lakeview Dashboards | Introducing AI/BI | BI Dashboard |
| Dashboard Parameters | Parameters | Dashboard Parameters | Dynamic HTML in Lakeview | BI Dashboard |
| Dashboard Permissions & Sharing | Share Dashboards | Dashboard Sharing | Dashboard Sharing Intro | Data Sharing |
| Dashboard Scheduling | Schedule & Subscribe | Dashboard Schedule | Dashboard Subscriptions | Lakehouse Monitoring |
| Configuring Alerts | Alerts | Alerts with DBSQL | Lakeview Alert Destinations | Lakehouse Monitoring |
| Effective Visualization Types | Dashboards | Creating Lakeview Dashboards | Introducing AI/BI | BI Dashboard |
Section 7: AI/BI Genie Spaces
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| Genie Purpose & Features | Genie Spaces | AI/BI Genie Enduser | Introducing AI/BI | AI/BI Genie Product |
| Creating Genie Spaces | Genie Spaces | AI/BI Genie Demo | Improve Genie Accuracy | AI/BI Genie |
| Genie Permissions & Distribution | Genie | AI/BI Genie | Dashboard Sharing | Data Sharing |
| Optimizing Genie Spaces | Genie Best Practices | – | Improve Genie Accuracy | – |
Section 8: Data Modeling
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| Data Modeling Techniques (Star, Snowflake, Data Vault) | Lakehouse Architecture | – | Data Vault Guidance | – |
| Medallion Architecture | Medallion Architecture | Medallion Architecture | What is Medallion Architecture? | Medallion Architecture |
Section 9: Securing Data
| Objective | Official Docs | Demo | Blog Post | Training Resource |
|---|---|---|---|---|
| Unity Catalog Security & Roles | Privilege Model | UC Privilege Model | ABAC in Unity Catalog | UC Training |
| Unity Catalog 3-Level Namespace | Unity Catalog | Introducing Unity Catalog | – | UC Training |
| Security Best Practices (PII, Table Ownership) | Security Best Practices | PII Detection & Masking | 10 Security Best Practices | Data Security |
How to Use This Table
- Official Docs: Authoritative Databricks documentation (docs.databricks.com)
- Demo: Interactive hands-on demonstrations and tutorials
- Blog Post: Technical articles with practical insights and best practices
- Training Resource: Courses, product pages, and learning materials
💡 Tip: For comprehensive study, review resources across all columns. Official docs provide technical accuracy, demos offer hands-on practice, blog posts share real-world experiences, and training resources give structured learning paths.
Additional Recommended Resources
General Certification Prep
- Data Analysis with Databricks training links
- AI/BI for Data Analysts (Self-paced)
- SQL Analytics on Databricks (Self-paced)
Official Documentation
Community Resources
Last Updated: November 2025 | Exam Version: October 2025
Related: What Changed in the 2025 Data Analyst Exam? (2023 vs 2025 Comparison)
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