Skills at a glance
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Maintain a data analytics solution (25–30%)
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Prepare data (45–50%)
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Implement and manage semantic models (25–30%)
Maintain a data analytics solution (25–30%)
Implement security and governance
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Implement workspace-level access controls
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Implement item-level access controls
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Implement row-level, column-level, object-level, and file-level access control
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Apply sensitivity labels to items
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Endorse items
Maintain the analytics development lifecycle
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Configure version control for a workspace
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Create and manage a Power BI Desktop project (.pbip)
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Create and configure deployment pipelines
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Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
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Deploy and manage semantic models by using the XMLA endpoint
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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
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Create a data connection
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Discover data by using OneLake data hub and real-time hub
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Ingest or access data as needed
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Choose between a lakehouse, warehouse, or eventhouse
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Implement OneLake integration for eventhouse and semantic models
Transform data
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Create views, functions, and stored procedures
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Enrich data by adding new columns or tables
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Implement a star schema for a lakehouse or warehouse
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Denormalize data
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Aggregate data
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Merge or join data
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Identify and resolve duplicate data, missing data, or null values
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Convert column data types
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Filter data
Query and analyze data
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Select, filter, and aggregate data by using the Visual Query Editor
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Select, filter, and aggregate data by using SQL
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Select, filter, and aggregate data by using KQL
Implement and manage semantic models (25–30%)
Design and build semantic models
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Choose a storage mode
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Implement a star schema for a semantic model
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Implement relationships, such as bridge tables and many-to-many relationships
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Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
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Implement calculation groups, dynamic format strings, and field parameters
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Identify use cases for and configure large semantic model storage format
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Design and build composite models
Optimize enterprise-scale semantic models
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Implement performance improvements in queries and report visuals
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Improve DAX performance
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Configure Direct Lake, including default fallback and refresh behavior
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Implement incremental refresh for semantic models
