Databricks Certified Data Analyst Associate Preparation Details
The Databricks Certified Data Analyst Associate exam validates your ability to manage, query, and visualize data using the Databricks Data Intelligence Platform. This guide walks through every domain in the official exam guide, from Unity Catalog and data ingestion to AI/BI dashboards and Genie spaces. You can also explore more Databricks certification study guides on the Databricks category to keep building your skills.
Databricks Certified Data Analyst Associate Materials
| Coursera | Mastering Azure Databricks for Data Engineers Specialization |
| Udemy | Databricks Data Analyst Associate: Practice Exams |
| Whizlabs | Databricks Certified Data Analyst Associate |
Section 1: Understanding of Databricks Data Intelligence Platform
Topics Covered
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.
AI and machine learning on Databricks
What is Delta Lake in Databricks?
Data warehousing on Databricks
Understand catalogs, schemas, managed and external tables, access controls, views, certified tables, and lineage within the Catalog Explorer interface.
Managed versus external assets in Unity Catalog
Flag certified and deprecated data
Unity Catalog privileges and securable objects
Describe the role and features of Databricks Marketplace.
What is Databricks Marketplace?
Section 2: Managing Data
Topics Covered
Use Unity Catalog to discover, query, and manage certified datasets.
Flag certified and deprecated data
Use the Catalog Explorer to tag a data asset and view its lineage.
Apply tags to Unity Catalog securable objects
Perform data cleaning on Unity Catalog Tables in SQL, including removing invalid data or handling missing values.
Clean and validate data with batch or stream processing
Section 3: Importing Data
Topics Covered
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.
What is Databricks Marketplace?
Use the Databricks Workspace UI to upload a data file to the platform.
Upload files to a Unity Catalog volume
Work with files in Unity Catalog volumes
Section 4: Executing queries using Databricks SQL and Databricks SQL Warehouses
Topics Covered
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.
Use materialized views in Databricks SQL
Use streaming tables in Databricks SQL
Perform aggregate operations such as count, approximate count distinct, mean, and summary statistics.
approx_count_distinct aggregate function
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.
Managed versus external assets in Unity Catalog
Use Delta Lake’s time travel to access and query historical data versions.
Section 5: Analyzing Queries
Topics Covered
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
Dataset optimization and caching
Apply Liquid Clustering to improve query speed when filtering large tables on specific columns.
Use liquid clustering for tables
Fix a query to achieve the desired results.
Section 6: Working with Dashboards and Visualizations in Databricks
Topics Covered
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.
Visualizations in Databricks notebooks and SQL editor
Work with parameters in SQL queries and dashboards, including defining, configuring, and testing parameters.
Work with dashboard parameters
Configure permissions through the UI to share dashboards with workspace users/groups, external users through shareable links, and embed dashboards in external apps.
Schedule an automatic dashboard refresh.
Manage scheduled dashboard updates and subscriptions
Configure an alert with a desired threshold and destination.
Identify the effective visualization type to communicate insights clearly.
AI/BI dashboard visualization types
Section 7: Developing, Sharing, and Maintaining AI/BI Genie spaces
Topics Covered
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.
Create and manage a Genie Agent
Use trusted assets in AI/BI Genie spaces
Assign permissions via the UI and distribute Genie spaces using embedded links and external app integrations.
Manage dashboard and Genie Space embedding
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.
Test and monitor a Genie Space
Use benchmarks in a Genie space
Section 8: Data Modeling with Databricks SQL
Topics Covered
Apply industry-standard data modeling techniques, such as star, snowflake, and data vault schemas, to analytical workloads.
Data Warehousing Modeling Techniques and Their Implementation
Understand how industry-standard models align with the Medallion Architecture.
What is the medallion lakehouse architecture?
Section 9: Securing Data
Topics Covered
Use Unity Catalog roles and sharing settings to ensure workspace objects are secure.
Unity Catalog privileges and securable objects
Understand how the 3-level namespace(Catalog / Schema / Tables or Volumes) works in the Unity Catalog.
Unity Catalog securable objects reference
What are catalogs in Databricks?
What are schemas in Databricks?
Apply best practices for storage and management to ensure data security, including table ownership and PII protection.
Manage Unity Catalog object ownership
Wrapping Up Databricks Certified Data Analyst Associate
That covers every domain in the Databricks Certified Data Analyst Associate exam guide, from managing and importing data in Unity Catalog to building AI/BI dashboards and Genie spaces. With consistent practice across these official documentation links, you will be well prepared to sit for the exam with confidence. You can also explore more Databricks certification study guides on the Databricks category to keep building your skills. Have a question or tip? Leave a comment below.
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