Databricks Certified Data Analyst Associate
Validates ability to perform data analysis tasks on the Databricks Data Intelligence Platform including managing and importing data, executing and analyzing queries with Databricks SQL, building dashboards and visualizations, developing AI/BI Genie spaces, modeling data with Databricks SQL, and applying data security practices. The exam consists of 45 scored multiple-choice questions over 90 minutes and covers nine domains: Understanding Databricks Data Intelligence Platform (11%), Managing Data (8%), Importing Data (5%), Executing Queries Using Databricks SQL (20%), Analyzing Queries (15%), Creating Dashboards and Visualizations (16%), Developing AI/BI Genie Spaces (12%), Data Modeling with Databricks SQL (5%), and Securing Data (8%). Recommended: 6+ months of hands-on data analysis experience.
Exam domains
- Executing Queries Using Databricks SQL20%
Author and run analytical queries against Unity Catalog using the Databricks SQL editor on Databricks SQL Warehouses, leveraging the Databricks Assistant for query generation, explanation, and debugging. Build cross-system analytics with Lakehouse Federation joins between Delta tables and federated sources, create streaming tables and materialized views, perform aggregations (count, approx_count_distinct, mean, summary statistics), execute joins and set operations, sort/filter results, create managed and external tables in Unity Catalog, and query historical versions with Delta Lake time travel.
- Creating Dashboards and Visualizations16%
Build AI/BI Dashboards with multi-tab/page layouts, multiple datasets, and widgets (visualizations, text, images), and create visualizations directly in notebooks and the SQL editor. Define and test SQL/dashboard parameters, schedule automatic dashboard refreshes, configure Databricks SQL alerts with thresholds and notification destinations (email, Slack), and share dashboards through workspace permissions, shareable links, and embedded apps.
- Analyzing Queries15%
Diagnose and improve query performance on Databricks SQL Warehouses using Photon, Query Insights, the Query Profiler, query history, and result caching, and audit historical results via Delta Lake history. Apply Liquid clustering to large tables to accelerate selective filters, and refactor queries (e.g., add missing GROUP BY clauses) to return the intended results.
Sources
Questions are grounded in 50 references from official and authoritative materials.