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This course covers every area of the DP-600 exam with 225+ questions (with answers and instruction). These areas include:Plan, implement, and manage a solution for data analytics (10-15%)Plan a data analytics environmentIdentify requirements for a solution, including components, features, performance, and capacity stock-keeping units (SKUs)Recommend settings in the Fabric admin portalChoose a data gateway typeCreate a custom Power BI report themeImplement and manage a data analytics environmentImplement workspace and item-level access controls for Fabric itemsImplement data sharing for workspaces, warehouses, and lakehousesManage sensitivity labels in semantic models and lakehousesConfigure Fabric-enabled workspace settingsManage Fabric capacityManage the analytics development lifecycleImplement version control for a workspaceCreate and manage a Power BI Desktop project (.pbip)Plan and implement deployment solutionsPerform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic modelsDeploy and manage semantic models by using the XMLA endpointCreate and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic modelsPrepare and serve data (40-45%)Create objects in a lakehouse or warehouseIngest data by using a data pipeline, dataflow, or notebookCreate and manage shortcutsImplement file partitioning for analytics workloads in a lakehouseCreate views, functions, and stored proceduresEnrich data by adding new columns or tablesCopy dataChoose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouseCopy data by using a data pipeline, dataflow, or notebookAdd stored procedures, notebooks, and dataflows to a data pipelineSchedule data pipelinesSchedule dataflows and notebooksTransform dataImplement a data cleansing processImplement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensionsImplement bridge tables for a lakehouse or a warehouseDenormalize dataAggregate or de-aggregate dataMerge or join dataIdentify and resolve duplicate data, missing data, or null valuesConvert data types by using SQL or PySparkFilter dataOptimize performanceIdentify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queriesImplement performance improvements in dataflows, notebooks, and SQL queriesIdentify and resolve issues with Delta table file sizesImplement and manage semantic models (20-25%)Design and build semantic modelsChoose a storage mode, including Direct LakeIdentify use cases for DAX Studio and Tabular Editor 2Implement a star schema for a semantic modelImplement relationships, such as bridge tables and many-to-many relationshipsWrite calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functionsImplement calculation groups, dynamic strings, and field parametersDesign and build a large format datasetDesign and build composite models that include aggregationsImplement dynamic row-level security and object-level securityValidate row-level security and object-level securityOptimize enterprise-scale semantic modelsImplement performance improvements in queries and report visualsImprove DAX performance by using DAX StudioOptimize a semantic model by using Tabular Editor 2Implement incremental refreshExplore and analyze data (20-25%)Perform exploratory analyticsImplement descriptive and diagnostic analyticsIntegrate prescriptive and predictive analytics into a visual or reportProfile dataQuery data by using SQLQuery a lakehouse in Fabric by using SQL queries or the visual query editorQuery a warehouse in Fabric by using SQL queries or the visual query editorConnect to and query datasets by using the XMLA endpoint

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