DATABRICKSDatabricks Certified Data Analyst Associate
The Databricks Certified Data Analyst Associate certification validates your ability to use the Databricks Data Intelligence Platform to perform introductory data analysis tasks. It is designed for data analysts who query, visualize, and manage data using Databricks SQL, Unity Catalog, and AI/BI Genie. Earning it demonstrates that you can turn raw data into trusted insights and share them through dashboards and visualizations.
817 practice questions · Updated 2026-07-30
9Domains
39Objectives
245Concepts
817Questions
DATA-ANALYST-ASSOCIATE Curriculum
Every domain, objective, and concept the DATA-ANALYST-ASSOCIATE exam measures.
- Databricks Intelligence Platform Overview
- Mosaic AI
- Delta Live Tables
- Lakeflow Jobs
- Data Intelligence Engine
- Delta Lake
- Unity Catalog
- Databricks SQL
- Catalogs
- Schemas
- Managed Tables
- External Tables
- Access Controls
- Views
- Certified Tables
- Lineage
- Catalog Explorer Interface
- Databricks Marketplace Overview
- Marketplace Roles and Permissions
- Listing Types and Formats
- Browsing and Searching Listings
- Listing Details and Metadata
- Consuming Listings
- Publishing and Managing Listings
- Security and Governance in Marketplace
- Discovering datasets in Unity Catalog
- Querying datasets in Unity Catalog
- Managing certified datasets
- Understanding Unity Catalog object hierarchy
- Using the Catalog Explorer
- Tagging data assets in Catalog Explorer
- Viewing data lineage in Catalog Explorer
- Removing invalid data
- Handling missing values
- Data cleaning with SQL functions
- Working with Unity Catalog tables
- S3 Ingestion
- Delta Sharing
- API-Driven Data Intake
- Auto Loader
- Databricks Marketplace
- Accessing the Databricks Workspace UI
- Uploading a data file
- Verifying upload success
- Accessing Databricks Assistant
- Using Assistant for Query Writing
- Debugging with Assistant
- Interpreting Assistant Suggestions
- SQL Warehouse Definition
- SQL Warehouse Architecture
- Query Execution Flow
- SQL Warehouse vs. Interactive Clusters
- Concurrency and Scaling
- Performance Optimization Features
- Federated data sources in Databricks SQL
- Setting up a connection to a federated data source
- Querying a federated data source
- Joining a Delta table with a federated data source
- Handling data types and schema differences
- Optimizing cross-system queries
- Materialized Views in Databricks SQL
- Creating Materialized Views
- Streaming Tables in Databricks
- Creating Streaming Tables
- When to Use Streaming Tables vs Materialized Views
- Dynamic Views vs Materialized Views
- Refreshing and Managing Materialized Views
- Limitations and Considerations
- COUNT function
- COUNT DISTINCT
- APPROX_COUNT_DISTINCT
- MEAN/AVG function
- Summary statistics functions
- GROUP BY with aggregates
- Inner Join
- Left Join
- Right Join
- Full Outer Join
- Cross Join
- Semi Join
- Anti Join
- Join with Single Key
- Join with Multiple Keys
- Join Syntax Variations
- Union Operation
- Union All Operation
- Set Operation Requirements
- Difference Between Union and Union All
- Difference Between Join Types
- Sorting results with ORDER BY
- Filtering rows with WHERE
- Combining sorting and filtering
- Using LIMIT to restrict results
- Managed vs External Tables
- Creating Managed Tables
- Creating External Tables
- Reading Data from Multiple Sources
- Joining Data from Multiple Sources
- Creating Tables from Query Results
- Unity Catalog Integration
- Table Creation with CTAS
- Data Format Considerations
- Unified Dataset Creation
- Time travel overview
- Querying by version number
- Querying by timestamp
- Time travel syntax in Databricks SQL
- Time travel with views and tables
- Time travel and table metadata
- Time travel and data retention
- Photon Overview
- Photon Benefits
- Supported Workloads
- Photon Features
- Enabling and Using Photon
- Query Insights overview
- Accessing Query Insights
- Interpreting Query Insights metrics
- Query Profiler log basics
- Reading the Query Profiler log
- Identifying common performance issues
- Using logs to diagnose bottlenecks
- Delta Lake transaction log
- DESCRIBE HISTORY command
- Interpreting history metadata
- Time travel queries
- Comparing historical results
- Validating query results
- Trend analysis over time
- Query History Overview
- Filtering and Searching Query History
- Understanding Query Metrics
- Identifying Performance Bottlenecks
- Caching Concepts
- Leveraging Result Caching
- Managing Cache Invalidation
- Best Practices for Query Optimization
- Liquid Clustering Fundamentals
- Clustering Columns Selection
- Enabling Liquid Clustering
- Query Acceleration with Clustering
- Maintenance and Overhead
- Identify query intent
- Diagnose syntax errors
- Diagnose logical errors
- Adjust SELECT clauses
- Adjust WHERE clauses
- Adjust JOIN conditions
- Adjust GROUP BY and aggregations
- Adjust ORDER BY and limits
- Test and validate results
- AI/BI Dashboards Overview
- Creating a Dashboard
- Multi-Tab Layouts
- Page Layout Design
- Adding Multiple Data Sources
- Managing Datasets
- Adding Visualizations
- Adding Text Widgets
- Adding Image Widgets
- Configuring Widget Properties
- Interactivity and Filters
- Publishing and Sharing
- Creating visualizations in notebooks
- Creating visualizations in SQL editor
- Selecting visualization types
- Configuring visualization settings
- Interacting with visualizations
- Defining parameters in SQL queries
- Configuring parameters in dashboards
- Testing parameters in queries and dashboards
- Dashboard refresh scheduling overview
- Accessing the schedule feature
- Configuring refresh frequency
- Setting time zone and start time
- Managing scheduled refreshes
- Handling refresh failures
- Alert configuration basics
- Setting alert thresholds
- Choosing alert destinations
- Testing and managing alerts
- Visualization Types
- Data-Encoding Mapping
- Chart Selection Criteria
- Comparative Analysis Visuals
- Part-to-Whole Visuals
- Trend and Time-Series Visuals
- Distribution Visuals
- Relationship Visuals
- Geospatial Visuals
- Visualization Best Practices
- Purpose of AI/BI Genie spaces
- Key features of AI/BI Genie spaces
- Components of AI/BI Genie spaces
- Defining sample questions
- Writing domain-specific instructions
- Selecting SQL warehouses
- Curating Unity Catalog datasets
- Vetting queries as Trusted Assets
- Genie space permissions overview
- Assigning permissions via UI
- Managing permission inheritance
- Generating embedded links for Genie spaces
- Distributing Genie spaces via external app integrations
- Best practices for sharing and maintaining access
- Tracking user questions
- Monitoring response accuracy
- Collecting and analyzing user feedback
- Updating instructions based on stakeholder input
- Updating trusted assets based on stakeholder input
- Validating accuracy with benchmarks
- Refreshing Unity Catalog metadata
- Star Schema
- Snowflake Schema
- Data Vault Schema
- Fact Tables
- Dimension Tables
- Schema Comparison
- Applying Schemas in Databricks SQL
- Medallion Architecture Overview
- Industry-Standard Models Alignment
- Bronze Layer Mapping
- Silver Layer Mapping
- Gold Layer Mapping
- Implementation Considerations
- Unity Catalog roles
- Sharing settings
- Securing workspace objects
- Three-level namespace structure
- Catalog concept
- Schema concept
- Table and volume distinction
- Namespace hierarchy and addressing
- Unity Catalog integration
- Table Ownership
- Setting Table Ownership
- Ownership and Permissions
- PII Protection Overview
- PII Identification
- PII Protection Techniques
- Storage Best Practices for Security
- Data Management for Security
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