DP-600: Choosing Power BI Semantic Model Storage Modes and Composite Models
Choose Import when interactive speed and modeling flexibility matter most and scheduled freshness is acceptable.
DP-600: Calculation Groups, Dynamic Format Strings, and Field Parameters in Power BI
Use a calculation group when the same calculation transformation must apply to many explicit measures, a dynamic format string when a measure's displayed format must change with context without converting its value to text, and a field…
DP-600: Advanced DAX Patterns for Enterprise Semantic Models
Build advanced Data Analysis Expressions (DAX) measures in stages: establish the required filter context, store meaningful intermediate results in variables, use iterators only when an expression must be evaluated row by row, shape table…
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DP-600: Choosing Power BI Semantic Model Storage Modes and Composite Models
Choose Import when interactive speed and modeling flexibility matter most and scheduled freshness is acceptable.
DP-600: Calculation Groups, Dynamic Format Strings, and Field Parameters in Power BI
Use a calculation group when the same calculation transformation must apply to many explicit measures, a dynamic format string when a measure's displayed format must change with context without converting its value to text, and a field…
DP-600: Advanced DAX Patterns for Enterprise Semantic Models
Build advanced Data Analysis Expressions (DAX) measures in stages: establish the required filter context, store meaningful intermediate results in variables, use iterators only when an expression must be evaluated row by row, shape table…
DP-600: Designing Power BI Semantic Models and Relationships in Microsoft Fabric
A reliable Power BI semantic model uses a star schema whose dimension tables have unique keys, whose fact tables retain a declared business grain, and whose relationships express intentional one-to-many filter paths.
DP-600: Querying Microsoft Fabric with Visual Query Editor, SQL, KQL, and DAX
Choose the query surface by the engine that owns the data and the kind of result the reader needs.
DP-600: Dimensional Modeling for Fabric Lakehouses and Warehouses
Design a dimensional model in a fixed sequence: define the business process and the grain of one fact row; identify facts that are valid at that grain; create conformed dimensions; replace unstable source identifiers with durable…
DP-600: Transforming and Cleaning Data in Microsoft Fabric
Choose the transformation tool according to the work it must own.
DP-600: OneLake Shortcuts and Integration Patterns for Fabric Analytics
OneLake shortcuts should expose existing data when the data can remain under its present owner, the consuming Fabric engine can read its format, and the identity path can be governed end to end.
DP-600: Discovering, Connecting, and Ingesting Data in Microsoft Fabric
An analytics engineer should make the acquisition decision in this order: discover a governed source, confirm its owner and intended analytical use, choose the identity that will run the connection, and then select the least complex…
DP-600: Managing the Microsoft Fabric Analytics Development Lifecycle
A Fabric analytics team should use one controlled path from editable source to a validated production release: develop source-control-friendly artifacts, review changes in Git, synchronize them to an isolated development workspace,…
DP-600: Securing and Governing Microsoft Fabric Analytics Solutions
Securing a Microsoft Fabric analytics solution requires several coordinated layers: use workspace roles for broad collaboration duties, item permissions for access to a specific asset, and OneLake security roles for the tables, folders,…
DP-600: Choosing Microsoft Fabric Analytics Stores: Lakehouse, Warehouse, Eventhouse, and Semantic Models
Choose a lakehouse when varied files and tables need Spark-led engineering, a Warehouse when structured analytical data needs a SQL-first relational experience, and an Eventhouse when high-volume event streams need low-latency analysis…