DP-600 Certification

The DP-600 certification exam validates your ability to implement analytics solutions using Microsoft Fabric, certifying you as a Fabric Analytics Engineer. 🧑‍🎓

The exam covers essential skills and key components such as warehouses, lakehouses, data pipelines, dataflows, and semantic models. The key skills required for the DP-600 exam include:

Maintain a data analytics solution (25–30%)
Prepare data (45–50%)
Implement and manage semantic models (25–30%)

This page provides a comprehensive overview of the key skills covered in the DP-600 exam. We provide step-by-step tutorials to each exam topic, helping you to prepare for the DP-600 exam and master the skills needed to become a certified Fabric Analytics Engineer. 🚀

Microsoft Fabric Book

We are delighted to publish a hands-on guide to implementing end-to-end data projects in Microsoft Fabric. This hands-on book walks you through the key components and functionalities of Microsoft Fabric and invites you to actively follow the steps yourself. Numerous visual elements are used to make learning even clearer. The explanations are illustrated by a fictional story about a futuristic data factory that symbolizes Microsoft Fabric.

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Maintain a data analytics solution (25–30%)

Implement security and governance

Implement workspace-level access controls
Implement item-level access controls
Implement row-level access control
- Implement static row-level access control for semantic models
- Implement row-level access control for warehouses
Implement column-level access control
Implement object-level access control
Implement file-level access control
Apply sensitivity labels to items
Endorse items

Maintain the analytics development lifecycle

Configure version control for a workspace
Create and manage a Power BI Desktop project (.pbip)
Create and configure deployment pipelines
Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
Deploy and manage semantic models by using the XMLA endpoint
Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models

Prepare data (45–50%)

Get data

Create a data connection
Discover data by using OneLake data hub and real-time hub
Ingest or access data as needed
Choose between a lakehouse, warehouse, or eventhouse
Implement OneLake integration for eventhouse and semantic models

Transform data

Create views, functions, and stored procedures
Enrich data by adding new columns or tables
Implement a star schema for a lakehouse or warehouse
Denormalize data
Aggregate data
Merge or join data
Identify and resolve duplicate data, missing data, or null values
Convert column data types
Filter data

Query and analyze data

Select, filter, and aggregate data by using the Visual Query Editor
Select, filter, and aggregate data by using SQL
Select, filter, and aggregate data by using KQL

Implement and manage semantic models (25–30%)

Design and build semantic models

Choose a storage mode
Implement a star schema for a semantic model
Implement relationships, such as bridge tables and many-to-many relationships
Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
Implement calculation groups, dynamic format strings, and field parameters
Identify use cases for and configure large semantic model storage format
Design and build composite models

Optimize enterprise-scale semantic models

Implement performance improvements in queries and report visuals
Improve DAX performance
Configure Direct Lake, including default fallback and refresh behavior
Implement incremental refresh for semantic models