Microsoft DP-600 Fabric Analytic Engineer Practice Exam Test

Ace your Exam in the first attempt!
What you'll learn:
Plan, implement, and manage a solution for data analytics
Prepare and serve data
Implement and manage semantic models
Explore and analyze data
Requirements:
Azure Fundamentals
Description:
These practice tests closely resemble real exam questions you may encounter in the DP-600 exam. They cover all areas of the syllabus and test your knowledge thoroughly. Since many answer options may seem correct, I've provided brief explanations for why certain options are incorrect. If you diligently work through these tests and stay dedicated to learning the Microsoft Fabric concepts, I guarantee you'll be prepared to pass the exam with confidence.Below are the skills that will be tested by Practice Tests.Skills at a glancePlan, implement, and manage a solution for data analytics (10-15%)Prepare and serve data (40-45%)Implement and manage semantic models (20-25%)Explore and analyze data (20-25%)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 endpointAdditional resourcesTrainingModuleExplore fundamentals of large-scale data analytics - TrainingOrganizations use analytics platforms to build large scale data analytics solutions that generate insights and drive success. Microsoft provides multiple technologies that you can combine to build a large scale data analytics solution.Certification
Who this course is for:
Solution architects Data engineers Data scientists AI engineers Database administrators Power BI data analysts
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