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- Expired on May 12, 2025
- Last Update: May 11, 2025
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Price:
44.99 $0 $
About This Course
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 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
