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Are you looking for the best source to prepare the DP-600 certification exam?Here is the ONLY Exam Preparation you need to PASS the DP-600 exam on the FIRST ATTEMPT and become Microsoft Certified: Fabric Analytics Associate Engineer!I know how it could be stressfull to prepare a certification and to retake it several time. I passed the DP-600 on the first attempst and I would like to share with you this exam test. Make your exam preparation effective and learn Fabric with Answers & Explantations & Documentations. This course exam test questions are very similar to the exam updated on July 2024 to maximize your chances of passing the exam on the first attempt.Why should you take this course?- This exam preparation includes updates make by Microsoft on July 2024.- The questions are very similar to the real exam,- The questions cover all the topics you need to know.- Every question has the correct answer- Every answer is accompanied by an explanation and/or a link to a certified documentation to learn more about Fabric.- This course teaches you everything you need to know about the exam itself - Don't waste your time, concentrate on what's really important to master the exam!About Instructor:My name is Valery, I'm a Certified Professional and I work as Senior Data Analyst and Data Engineer. My goal is to help people advance in their careers by learning new data analysis tools. I look forward to providing you with the skills you need to master data analysis! If you're looking for quality AND affordable training, then take this course! Skills measured in this test as of July 22, 20241. 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 theme Implement 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 capacity and configure capacity settings Manage 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 models2. Prepare 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 notebookImplement Fast Copy when using dataflowsAdd 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 the structure or size of Delta table files (including v-order and optimized writes)3. Implement 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 security Optimize 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 refresh4. Explore 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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