Databricks Certified Data Analyst Associate Study Guide

Domain 1: Understanding of Databricks Data Intelligence Platform

Key concepts you must know · 84 practice questions

Describe the core components of the platform: Mosaic AI, Delta Live Tables, Lakeflow Jobs, the Data Intelligence Engine, Delta Lake, Unity Catalog, and Databricks SQL.
Understand catalogs, schemas, managed and external tables, views, certified tables, access controls, and lineage in Catalog Explorer.
Describe the role and features of Databricks Marketplace for discovering and sharing data and assets.

Domain 2: Managing Data

Key concepts you must know · 66 practice questions

Use Unity Catalog to discover, query, and manage certified datasets.
Use Catalog Explorer to tag a data asset and view its lineage.
Clean data on Unity Catalog tables in SQL, removing invalid rows and handling missing values.

Domain 3: Importing Data

Key concepts you must know · 50 practice questions

Explain the ways to bring data into Databricks: S3 ingestion, Delta Sharing with external systems, API-driven intake, Auto Loader, and Marketplace.
Use the Databricks workspace UI to upload a data file to the platform.

Domain 4: Executing queries using Databricks SQL and Databricks SQL Warehouses

Key concepts you must know · 152 practice questions

Use the Databricks Assistant in a notebook or the SQL editor to help write and debug queries.
Explain the role a SQL warehouse plays in query execution.
Query across systems by joining a Delta table with a federated data source.
Create a materialized view and know when to use streaming tables versus materialized views, and how dynamic views differ from materialized views.
Perform aggregates (count, approximate count distinct, mean, summary statistics), joins (inner, left, right), and set operations (union, union all), plus sorting and filtering.
Create managed and external tables, including unified datasets joined from CSV, Parquet, and Delta sources in Unity Catalog.
Use Delta Lake time travel to query historical versions of a table.

Domain 5: Analyzing Queries

Key concepts you must know · 108 practice questions

Understand the features, benefits, and supported workloads of Photon.
Identify poorly performing queries with Query Insights and the Query Profiler.
Use Delta Lake history to audit changes, validate results, and compare historical trends.
Use query history and result caching to cut development time and query latency.
Apply Liquid Clustering to speed up queries that filter large tables on specific columns.
Debug and fix a query that returns incorrect results.

Domain 6: Working with Dashboards and Visualizations in Databricks

Key concepts you must know · 120 practice questions

Build AI/BI Dashboards with multiple pages, multiple datasets, and widgets for visualizations, text, and images.
Create visualizations in notebooks and the SQL editor.
Define, configure, and test parameters in SQL queries and dashboards.
Share dashboards through UI permissions, shareable links for external users, and embedding in external apps.
Schedule automatic dashboard refresh and configure an alert with a threshold and destination.
Choose the visualization type that communicates an insight most clearly.

Domain 7: Developing, Sharing, and Maintaining AI/BI Genie spaces

Key concepts you must know · 92 practice questions

Describe the purpose, features, and components of AI/BI Genie spaces for natural-language analytics.
Create a Genie space with sample questions, domain instructions, a SQL warehouse, curated Unity Catalog datasets, and vetted Trusted Assets.
Assign permissions and distribute a Genie space with embedded links and external app integrations.
Optimize a Genie space by tracking questions, accuracy, and feedback, updating instructions and trusted assets, and refreshing Unity Catalog metadata.

Domain 8: Data Modeling with Databricks SQL

Key concepts you must know · 50 practice questions

Apply standard modeling techniques such as star, snowflake, and data vault schemas to analytical workloads.
Understand how these models align with the Medallion Architecture of bronze, silver, and gold layers.

Domain 9: Securing Data

Key concepts you must know · 66 practice questions

Use Unity Catalog roles and sharing settings to keep workspace objects secure.
Understand the three-level namespace of catalog, schema, and table or volume.
Apply storage and management best practices, including table ownership and PII protection.

Databricks Certified Data Analyst Associate exam tips

Concentrate on Executing Queries (20%), Working with Dashboards and Visualizations (16%), and Analyzing Queries (15%): together they are just over half the exam. Be fluent with joins, aggregates, materialized versus streaming tables, Delta time travel, AI/BI Dashboards, and performance tools like Photon, the Query Profiler, and Liquid Clustering.
Use current product names. The exam says AI/BI Dashboards and AI/BI Genie spaces, not Lakeview. Genie is now a full 12 percent domain, so know how to build, share, and optimize a Genie space.
Know the managed-versus-external table rule cold: dropping a managed table deletes the underlying data, while dropping an external table leaves the data in place. Similar distinctions (materialized view versus streaming table versus dynamic view) are frequent question material.
Understand the Unity Catalog three-level namespace (catalog.schema.table) and how it drives both querying and security, including roles, ownership, and PII protection.
For performance questions, match the tool to the symptom: Photon for faster execution, the Query Profiler and Query Insights to find slow queries, result caching to cut repeat latency, and Liquid Clustering to speed filtered scans on large tables.

Study guide FAQ

How many questions are on the exam and what is the passing score?

There are 45 scored multiple-choice questions in 90 minutes. Databricks does not publish a fixed passing percentage; you receive a pass or fail result. The certification is valid for two years and recertification requires taking the current live exam.

Do I need deep programming skills?

No. This is an analyst exam focused on SQL analysis, dashboards, and the Databricks SQL experience. You should be comfortable writing SQL (joins, aggregates, filtering) and using Databricks SQL, Unity Catalog, and AI/BI dashboards, but you are not expected to build data pipelines or write production Python.

Is the exam still five domains?

No. The current October 2025 version has nine domains, including newer areas such as Analyzing Queries (Photon, Query Profiler, Liquid Clustering), AI/BI Genie spaces, Data Modeling with Databricks SQL, and Securing Data. Older five-domain descriptions are out of date.

How is this different from the Data Engineer Associate exam?

The Data Analyst Associate is about querying, analyzing, visualizing, and securing data with Databricks SQL and AI/BI. The Data Engineer Associate is about building and maintaining data pipelines with Delta Lake, Spark, Auto Loader, Delta Live Tables, and Workflows.

Is CertGrid practice official Databricks material?

No. CertGrid is an independent practice platform and is not affiliated with or endorsed by Databricks. These questions are original and written to mirror the current exam guide so you can rehearse the objectives. Always confirm the current exam guide on the Databricks certification site before your exam.

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Test yourself: 788 Databricks Certified Data Analyst Associate practice questions