Chapter 3: Maintain a data analytics solution -Implement security and governance-Implement row-level, column-level, object-level, and file-level access control
Chapter 9: Maintain a data analytics solution -Maintain the analytics development lifecycle-Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
Chapter 10: Maintain a data analytics solution -Maintain the analytics development lifecycle-Deploy and manage semantic models by using the XMLA endpoint
Chapter 11: Maintain a data analytics solution -Maintain the analytics development lifecycle-Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models
Chapter 31: Implement and manage semantic models -Design and build semantic models-Implement relationships, such as bridge tables and many-to-many relationships
Chapter 32: Implement and manage semantic models -Design and build semantic models-Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
Chapter 33: Implement and manage semantic models -Design and build semantic models-Implement calculation groups, dynamic format strings, and field parameters
Chapter 34: Implement and manage semantic models -Design and build semantic models-Identify use cases for and configure large semantic model storage format
Chapter 38: Implement and manage semantic models -Optimize enterprise-scale semantic models-Configure Direct Lake, including default fallback and refresh behavior
