DP-600: Implementing Analytics Solutions Using Microsoft Fabric

Exam Quick Facts

Study Resources

Skills at a Glance

Who is this exam for?

This Microsoft Data certification covers data concepts and Azure data services. It tests your ability to work with relational and non-relational databases, analytics workloads, and data platforms on Azure. This is an associate-level exam that expects hands-on experience. You should have practical knowledge of the technologies covered.

Skills Measured

Maintain a data analytics solution (25–30%)

This domain covers monitoring and maintenance. You need to know how to use monitoring tools, configure alerts, and implement backup and recovery solutions.

Implement security and governance

• Implement workspace-level access controls
• Implement item-level access controls
• Implement row-level, column-level, object-level, and 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, 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%)

This domain covers data preparation. You need to know how to connect to data sources, clean and transform data, and prepare it for analysis.

Get data

• Create a data connection
• Discover data by using OneLake catalog and Real-Time hub
• Ingest or access data as needed
• Choose between different data stores
• 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
• Select, filter, and aggregate data by using DAX

Implement and manage semantic models (25–30%)

This domain covers the skills needed to work with the topics described below. Study each objective carefully and use the linked resources to deepen your understanding.

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
• Choose between Direct Lake on OneLake and Direct Lake on SQL analytics endpoint
• Implement incremental refresh for semantic models

Quick Links

• Official Exam Page
• Microsoft Study Guide
• Practice Assessment