Skills at a glance

• Maintain a data analytics solution (25–30%)

• Prepare data (45–50%)

• Implement and manage semantic models (25–30%)

Maintain a data analytics solution (25–30%)

Prepare data (45–50%)

Implement and manage semantic models (25–30%)

Maintain a data analytics solution (25–30%)

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

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, 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

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

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

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 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

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

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