Databricks Certified Data Analyst Associate Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Understanding of Databricks Data Intelligence Platform (8% of the exam)

Understanding of Databricks Data Intelligence Platform

• 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
• Understand catalogs, schemas, managed and external tables, access controls, views, certified tables, and lineage within the Catalog Explorer interface.
• Describe the role and features of Databricks Marketplace

Section summary: This section focuses on building familiarity with the foundational components that make up the Databricks Data Intelligence Platform. You will learn what each core service provides, how they interact with one another, and why they are vital to modern analytics workflows. Recognizing the value of features such as Unity Catalog and the Data Intelligence Engine helps you understand how Databricks streamlines data management, governance, and querying.

Just as important, this section ensures you are comfortable navigating with Catalog Explorer, exploring object hierarchies, and identifying the benefits of Marketplace as a source for reusable assets. By the end, you will see how data intelligence flows end-to-end within Databricks, from ingestion and management through to discovery and collaboration across teams.

Domain 2: Managing Data (12% of the exam)

Managing Data

• Use Unity Catalog to discover, query, and manage certified datasets
• Use the Catalog Explorer to tag a data asset and view its lineage
• Perform data cleaning on Unity Catalog Tables in SQL, including removing invalid data or handling missing values

Section summary: This section highlights how to manage and prepare data efficiently in Unity Catalog. Emphasis is placed on discovery, querying certified assets, and applying governance with tagging and lineage so that data remains clean, reliable, and auditable. You will also learn to use SQL operations for data cleaning and correcting inconsistencies.

The goal is to elevate your confidence in handling datasets that meet enterprise-level security and compliance standards. By being able to trace asset lineage, apply governance controls, and prepare data at scale, you will build a foundation for trustworthy analysis across dynamic and collaborative environments.

Domain 3: Importing Data (6% of the exam)

Importing Data

• 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.
• Use the Databricks Workspace UI to upload a data file to the platform.

Section summary: This section introduces the many ways data can be ingested into the Databricks ecosystem. You will explore common integration paths like cloud storage ingestion, Delta Sharing for external collaboration, APIs for real-time intake, and Auto Loader for automating streaming-like ingestion from large datasets. Marketplace is also part of the toolkit for bringing in shared, prebuilt assets.

Additionally, working directly in the Databricks UI to upload data teaches you how to quickly prototype with files and connect ingestion methods to projects. With this knowledge, you can confidently integrate data from diverse sources and ensure a consistent process for scaling ingestion workflows within your organization.

Domain 4: Executing queries using Databricks SQL and Databricks SQL Warehouses (28% of the exam)

Executing queries using Databricks SQL and Databricks SQL Warehouses

• Utilize Databricks Assistant within a Notebook or SQL Editor to facilitate query writing and debugging.
• Explain the role a SQL Warehouse plays in query execution.
• Querying cross-system analytics by joining data from a Delta table and a federated data source.
• Create a materialized view, including knowing when to use Streaming Tables and Materialized Views, and differentiate between dynamic and materialized views
• Perform aggregate operations such as count, approximate count distinct, mean, and summary statistics.
• 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).
• Perform sorting and filtering operations on a table
• 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
• Use Delta Lake's time travel to access and query historical data versions.

Section summary: This is the largest section of the exam and it ensures you can confidently use SQL within Databricks SQL Warehouses. You will learn to build queries across various tables and formats, apply aggregation, perform different types of joins, and leverage advanced features like time travel in Delta Lake. Understanding the difference between managed versus external tables, as well as when to apply materialized or streaming views, will solidify your ability to execute queries effectively.

The focus is not just on syntax but on knowing how to optimize your approach and combine multiple inputs for richer analytics. By using tools like Databricks Assistant for query support, you will also gain practical experience accelerating your workflow. These capabilities prepare you to navigate real-world analytics scenarios where speed, accuracy, and flexibility are critical.

Domain 5: Analyzing Queries (14% of the exam)

Analyzing Queries

• Understand the Features, Benefits, and Supported Workloads of Photon
• Identify poorly performing queries in the Databricks Intelligence platform, such as Query Insights, Query Profiler log, etc.
• Utilize Delta Lake to audit and view history, validate results, and compare historical results or trends.
• Utilize query history and caching to reduce development time and query latency
• Apply Liquid Clustering to improve query speed when filtering large tables on specific columns.
• Fix a query to achieve the desired results.

Section summary: This section centers on the performance and optimization of queries in Databricks. You will learn how to diagnose slow or poorly written queries using available profiling tools and metrics, then implement strategies to improve query performance through tools like Photon and Liquid Clustering.

You will also work with features that enhance data reliability and confidence, such as auditing history via Delta Lake. Leveraging caching, query history, and data validation builds efficiency and ensures accuracy in analysis. Altogether, this knowledge enables you to handle both troubleshooting and performance optimization with assurance.

Domain 6: Working with Dashboards and Visualizations in Databricks (12% of the exam)

Working with Dashboards and Visualizations in Databricks

• Build dashboards using AI/BI Dashboards, including multi-tabs/page layouts, multiple data sources/datasets, and widgets (visualizations, text, images)
• Create visualizations in notebooks and the SQL editor
• Work with parameters in SQL queries and dashboards, including defining, configuring, and testing parameters
• Configure permissions through the UI to share dashboards with workspace users/groups, external users through shareable links and embedd dashboards in external apps
• Schedule an automatic dashboard refresh.
• Configure an alert with a desired threshold and destination.
• Identify the effective visualization type to communicate insights clearly

Section summary: This section ensures you know how to transform data insights into compelling dashboards and visualizations. You will learn to create dashboards with interactive layouts, work with multiple data sources, and incorporate parameters for highly flexible insights. The exam also covers setting up scheduled refreshes and configuring alerts to keep insights current and actionable.

Furthermore, you will focus on sharing results effectively through permissions, external shareable links, and embedding dashboards into other tools. Identifying the right visualization for the intended message ensures your analysis communicates insights clearly and persuasively across stakeholders.

Domain 7: Developing, Sharing and Maintaining AI/BI Genie spaces (8% of the exam)

Developing, Sharing and Maintaining AI/BI Genie spaces

• Describe the purpose, key features and components of AI/BI Genie spaces
• 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.
• Assign permissions via the UI and distribute Genie spaces using embedded links and external app integrations
• 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

Section summary: This section is about creating and managing collaborative AI/BI Genie spaces. You will examine how Genie spaces allow teams to standardize analytics experiences by predefining questions, curating trusted datasets, and leveraging SQL warehouses. Setting permissions and sharing assets ensures colleagues and stakeholders can benefit from guided, domain-relevant analytics.

The maintenance aspect is equally important, emphasizing continuous improvement of Genie spaces through user feedback, accuracy benchmarking, and content updates. By curating Genie spaces with care and optimization, you will showcase the ability to maintain an ever-improving environment that delivers actionable and precise insights.

Domain 8: Data Modeling with Databricks SQL (6% of the exam)

Data Modeling with Databricks SQL

• Apply industry-standard data modeling techniques such as star, snowflake, and data vault schemas to analytical workloads.
• Understand how industry-standard models align with the Medallion Architecture.

Section summary: This section focuses on the principles of data modeling, one of the most essential skills for analysts. You will explore classical modeling concepts such as star, snowflake, and data vault models, and how they can be applied within Databricks SQL. Understanding why and when to use these models will equip you with the structure to organize analytical workloads effectively.

Aligning these industry-standard models with the Medallion Architecture introduces an enterprise-grade approach to analytics. This provides clarity on staging raw data into bronze, silver, and gold layers for scalability and quality. With this foundation, you gain the skills to ensure your data models deliver both performance and business insight.

Domain 9: Securing Data (6% of the exam)

Securing Data

• Use Unity Catalog roles and sharing settings to ensure workspace objects are secure.
• Understand how the 3-level namespace (Catalog / Schema / Tables or Volumes) works in the Unity Catalog
• Apply best practices for storage and management to ensure data security, including table ownership and PII protection.

Section summary: This section ensures that you understand how to safeguard data assets with the right access and governance. Unity Catalog plays a central role in security, with roles and share settings being key to protecting sensitive datasets and workspace objects. Grasping the three-level namespace also helps you manage how catalogs, schemas, and tables are structured for secure operations.

Attention is given to applying best practices that maintain compliance and confidentiality, particularly around PII. This equips you with tangible skills for securing data as you prepare insights from Databricks. Ultimately, this focus reflects the critical importance of aligning analytics with enterprise governance.