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In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.As a candidate for this exam, you should have subject matter expertise in designing, creating, and managing analytical assets, such as semantic models, data warehouses, or lakehouses.Your responsibilities for this role include:Prepare and enrich data for analysisSecure and maintain analytics assetsImplement and manage semantic modelsYou work closely with stakeholders for business requirements and partner with architects, analysts, engineers, and administrators.You should also be able to query and analyze data by using Structured Query Language (SQL), Kusto Query Language (KQL), and Data Analysis Expressions (DAX).Skills at a glanceMaintain a data analytics solution (25-30%)Prepare data (45-50%)Implement and manage semantic models (25-30%)Maintain a data analytics solution (25-30%)Implement security and governanceImplement workspace-level access controlsImplement item-level access controlsImplement row-level, column-level, object-level, and file-level access controlApply sensitivity labels to itemsEndorse itemsMaintain the analytics development lifecycleConfigure version control for a workspaceCreate and manage a Power BI Desktop project (.pbip)Create and configure deployment pipelinesPerform 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 data (45-50%)Get dataCreate a data connectionDiscover data by using OneLake data hub and real-time hubIngest or access data as neededChoose between a lakehouse, warehouse, or eventhouseImplement OneLake integration for eventhouse and semantic modelsTransform dataCreate views, functions, and stored proceduresEnrich data by adding new columns or tablesImplement a star schema for a lakehouse or warehouseDenormalize dataAggregate dataMerge or join dataIdentify and resolve duplicate data, missing data, or null valuesConvert column data typesFilter dataQuery and analyze dataSelect, filter, and aggregate data by using the Visual Query EditorSelect, filter, and aggregate data by using SQLSelect, filter, and aggregate data by using KQLImplement and manage semantic models (25-30%)Design and build semantic modelsChoose a storage modeImplement 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 format strings, and field parametersIdentify use cases for and configure large semantic model storage formatDesign and build composite modelsOptimize enterprise-scale semantic modelsImplement performance improvements in queries and report visualsImprove DAX performanceConfigure Direct Lake, including default fallback and refresh behaviourImplement incremental refresh for semantic models

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