DP-600 Preparation Details
Preparing for the DP-600 Implementing Analytics Solutions Using Microsoft Fabric exam? Don’t know where to start? This post is the DP-600 Certificate Study Guide (with links to each exam objective).
I have curated a list of articles from Microsoft documentation for each objective of the DP-600 exam. Please share the post within your circles so it helps them to prepare for the exam.
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DP-600 Microsoft Fabric Practice Test
| Udemy | Fabric Data Analytics Engineer |
| Coursera | Microsoft Fabric Analytics Engineer |
| Whizlabs | Microsoft Fabric Analytics Engineer |
Maintain a data analytics solution (25–30%)
Implement security and governance
Implement workspace-level access controls
Roles in workspaces in Microsoft Fabric
Give users access to workspaces
Implement item-level access controls
Share items in Microsoft Fabric
Implement row-level, column-level, object-level, and file-level access control
Row-level security (RLS) with Fabric data warehousing
Column-level security in Fabric data warehousing
OneLake security and access control
Apply sensitivity labels to items
Information protection in Microsoft Fabric
Apply sensitivity labels in Microsoft Fabric
Endorse items
Endorse Fabric and Power BI items
Maintain the analytics development lifecycle
Configure version control for a workspace
Microsoft Fabric Git integration
Configure Git integration for a Microsoft Fabric workspace
Create and manage a Power BI Desktop project (.pbip)
Power BI Desktop projects (.pbip)
Manage Power BI projects in Git
Create and configure deployment pipelines
Introduction to deployment pipelines
Get started with deployment pipelines
Perform impact analysis of downstream dependencies from lakehouses, warehouses, dataflows, and semantic models
Find downstream impact of changes in Microsoft Fabric
Deploy and manage semantic models by using the XMLA endpoint
Semantic model connectivity with the XMLA endpoint
Troubleshoot XMLA endpoint connectivity
Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models
Create and use report templates in Power BI Desktop
Using personal data source (.pbids) files to get data
Share access to a semantic model
Prepare data (45–50%)
Get data
Create a data connection
Discover data by using OneLake catalog and Real-Time hub
Explore All data streams in Fabric Real-Time hub
OneLake, the OneDrive for data
Ingest or access data as needed
Ingest data into the Lakehouse
How to copy data using copy activity
Get data from files into a Lakehouse
Choose between different data stores
Fabric decision guide – choose a data store
Data storage options in Microsoft Fabric
Better together: the lakehouse and warehouse
Implement OneLake integration for Eventhouse and semantic models
Eventhouse OneLake Availability
OneLake integration for semantic models
Integrate Microsoft Fabric with external systems
Transform data
Create views, functions, and stored procedures
Create and Use Views in Fabric Warehouse
Transform data with a stored procedure in a Warehouse
What is Fabric User data functions?
Enrich data by adding new columns or tables
Prepare and transform data in the lakehouse
Transform and Enrich Data with AI Functions – Microsoft Fabric
Implement a star schema for a lakehouse or warehouse
Understand star schema and the importance for Power BI
Dimensional modeling in Fabric Data Warehouse
Denormalize data
Power BI modeling guidance for Power Platform
Power BI model relationships guidance
Aggregate data
Group by or summarize rows in Power Query
Merge or join data
Identify and resolve duplicate data, missing data, or null values
Dedupe rows and find nulls by using data flow snippets
Validate functional dependencies in data with semantic link
Convert column data types
Change column data types in Power BI Desktop
Filter data
Filter activity – Microsoft Fabric
Filter transformation in mapping data flow
Use visual and page-level filters
Query and analyze data
Select, filter, and aggregate data by using the Visual Query Editor
Query the warehouse using the Visual Query editor
Query using the visual query editor
Select, filter, and aggregate data by using SQL
Query the SQL analytics endpoint or Warehouse in Microsoft Fabric
T-SQL surface area in Microsoft Fabric
Select, filter, and aggregate data by using KQL
Learn how to Query sample data
Kusto Query Language (KQL) overview
Select, filter, and aggregate data by using DAX
Implement and manage semantic models (25–30%)
Design and build semantic models
Choose a storage mode
Use storage mode in Power BI Desktop
Semantic model modes in the Power BI service
Implement a star schema for a semantic model
Dimensional modeling in Fabric Data Warehouse
Importance of star schema in Power BI
Implement relationships, such as bridge tables and many-to-many relationships
Create and manage relationships in Power BI Desktop
Many-to-many relationship guidance
Relationship troubleshooting guidance
Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
Introducing window functions in DAX
Implement calculation groups, dynamic format strings, and field parameters
Let report readers use field parameters to change visuals
Dynamic format strings for measures
Identify use cases for and configure large semantic model storage format
Large semantic model storage format
Large semantic models in Power BI Premium
Design and build composite models
Use composite models in Power BI Desktop
Composite model guidance in Power BI Desktop
Optimize enterprise-scale semantic models
Implement performance improvements in queries and report visuals
Optimization guide for Power BI
Use Performance Analyzer to examine report element performance
Improve DAX performance
Optimization guide for Power BI
Configure Direct Lake, including default fallback and refresh behavior
Direct Lake overview – Microsoft Fabric
How Direct Lake works – Microsoft Fabric
Choose between Direct Lake on OneLake and Direct Lake on SQL endpoints
Direct Lake on OneLake and Direct Lake on SQL endpoints
Implement incremental refresh for semantic models
Incremental refresh for semantic models in Power BI
Configure incremental refresh and real-time data
This brings us to the end of the DP-600 Implementing Analytics Solutions Using Microsoft Fabric Study Guide.
What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!
In case you are preparing for other Fabric certification exams, check out the Microsoft Fabric study guide for those exams.
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