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Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
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About the exam
Languages
Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured as of March 11, 2026
Audience profile
As a candidate for this Microsoft Certification, you should have subject matter expertise in integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining workloads in Azure Databricks. You should also have experience applying data quality and data governance best practices in Unity Catalog.
You need to know how to ingest and transform data by using Structured Query Language (SQL) and Python. You need experience with software development lifecycle (SDLC) practices, including Git. Additionally, you should be familiar with Microsoft Entra, Azure Data Factory, and Azure Monitor.
Your responsibilities for this role include:
Setting up and configuring an Azure Databricks environment.
Securing and governing Unity Catalog objects.
Preparing and processing data.
Deploying and maintaining data pipelines and workloads.
You work closely with administrators, platform architects, solution architects, data scientists, and data analysts to design, deploy, and secure data engineering solutions by using Azure Databricks.
Skills at a glance
Set up and configure an Azure Databricks environment (15–20%)
Secure and govern Unity Catalog objects (15–20%)
Prepare and process data (30–35%)
Deploy and maintain data pipelines and workloads (30–35%)
Set up and configure an Azure Databricks environment (15–20%)
Select and configure compute in a workspace
Choose an appropriate compute type, including job compute, serverless, warehouse, classic compute, and shared compute
Configure compute performance settings, including CPU, node count, autoscaling, termination, node type, cluster size, and pooling
Configure compute feature settings, including Photon acceleration, Azure Databricks runtime/Spark version, and machine learning
Install libraries for a compute resource
Configure access permissions to a compute resource
Create and organize objects in Unity Catalog
Apply naming conventions based on requirements, including isolation, development environment, and external sharing
Create a catalog based on requirements
Create a schema based on requirements
Create volumes based on requirements
Create tables, views, and materialized views
Implement a foreign catalog by configuring connections
Implement data definition language (DDL) operations on managed and external tables
Configure AI/BI Genie instructions for data discovery
Secure and govern Unity Catalog objects (15–20%)
Secure Unity Catalog objects
Grant privileges to a principal (user, service principal, or group) for securable objects in Unity Catalog
Implement table- and column-level access control and row-level security
Access Azure Key Vault secrets from within Azure Databricks
Authenticate data access by using service principals
Authenticate resource access by using managed identities
Govern Unity Catalog objects
Create, implement, and preserve table and column definitions and descriptions for data discovery
Configure attribute-based access control (ABAC) by using tags and policies
Configure row filters and column masks
Apply data retention policies
Set up and manage data lineage tracking by using Catalog Explorer, including owner, history, dependencies, and lineage
Configure audit logging
Design and implement a secure strategy for Delta Sharing
Prepare and process data (30–35%)
Design and implement data modeling in Unity Catalog
Design logic for data ingestion and data source configuration, including extraction type and file type
Choose an appropriate data ingestion tool, including Lakeflow Connect, notebooks, and Azure Data Factory
Choose a data loading method, including batch and streaming
Choose a data table format, such as Parquet, Delta, CSV, JSON, or Iceberg
Design and implement a data partitioning scheme
Choose a slowly changing dimension (SCD) type
Choose granularity on a column or table based on requirements
Design and implement a temporal (history) table to record changes over time
Design and implement a clustering strategy, including liquid clustering, Z-ordering, and deletion vectors
Choose between managed and unmanaged tables
Ingest data into Unity Catalog
Ingest data by using Lakeflow Connect, including batch and streaming
Ingest data by using notebooks, including batch and streaming
Ingest data by using SQL methods, including CREATE TABLE … AS (CTAS), CREATE OR REPLACE TABLE, and COPY INTO
Ingest data by using a change data capture (CDC) feed
Ingest data by using Spark Structured Streaming
Ingest streaming data from Azure Event Hubs
Ingest data by using Lakeflow Spark Declarative Pipelines, including Auto Loader
Cleanse, transform, and load data into Unity Catalog
Profile data to generate summary statistics and assess data distributions
Choose appropriate column data types
Identify and resolve duplicate, missing, and null values
Transform data, including filtering, grouping, and aggregating data
Transform data by using join, union, intersect, and except operators
Transform data by denormalizing, pivoting, and unpivoting data
Load data by using merge, insert, and append operations
Implement and manage data quality constraints in Unity Catalog
Implement validation checks, including nullability, data cardinality, and range checking
Implement data type checks
Implement schema enforcement and manage schema drift
Manage data quality with pipeline expectations in Lakeflow Spark Declarative Pipelines
Deploy and maintain data pipelines and workloads (30–35%)
Design and implement data pipelines
Design order of operations for a data pipeline
Choose between notebook and Lakeflow Spark Declarative Pipelines
Design task logic for Lakeflow Jobs
Design and implement error handling in data pipelines, notebooks, and jobs
Create a data pipeline by using a notebook, including precedence constraints
Create a data pipeline by using Lakeflow Spark Declarative Pipelines
Implement Lakeflow Jobs
Create a job, including setup and configuration
Configure job triggers
Schedule a job
Configure alerts for a job
Configure automatic restarts for a job or a data pipeline
Implement development lifecycle processes in Azure Databricks
Apply version control best practices using Git
Manage branching, pull requests, and conflict resolution
Implement a testing strategy, including unit tests, integration tests, end-to-end tests, and user acceptance testing (UAT)
Configure and package Databricks Asset Bundles
Deploy a bundle by using the Azure Databricks command-line interface (CLI)
Deploy a bundle by using REST APIs
Monitor, troubleshoot, and optimize workloads in Azure Databricks
Monitor and manage cluster consumption to optimize performance and cost
Troubleshoot and repair issues in Lakeflow Jobs, including repair, restart, stop, and run functions
Troubleshoot and repair issues in Apache Spark jobs and notebooks, including performance tuning, resolving resource bottlenecks, and cluster restart
Investigate and resolve caching, skewing, spilling, and shuffle issues by using a Directed Acyclic Graph (DAG), the Spark UI, and query profile
Optimize Delta tables for performance and cost, including OPTIMIZE and VACUUM commands
Implement log streaming by using Log Analytics in Azure Monitor
Configure alerts by using Azure Monitor
Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.
| Study resources | Links to learning and documentation |
|---|---|
| Get trained | Choose from self-paced learning paths and modules or take an instructor-led course |
| Find documentation | Azure Databricks Azure Data Factory Microsoft Entra Azure Monitor |
| Ask a question | Microsoft Q&A | Microsoft Docs |
| Get community support | Analytics on Azure - Microsoft Tech Community Azure Databricks - Community Hub |
| Follow Microsoft Learn | Microsoft Learn - Microsoft Tech Community |
| Find a video | Exam Readiness Zone Data Exposed Browse other Microsoft Learn shows |