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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%)

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 un proyecto de Power BI Desktop (.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