Overview

PRACTICE - PRACTICE - PRACTICE: PRACTICE WILL MAKE YOU PERFECT & become Microsoft Certified: Fabric Analytics Engineer AssociateCourse provides several practice sets similar to actual exam questions as per official exam syllabus and study guide for DP-600: Implementing Analytics Solutions Using Microsoft Fabric.To excel in this exam, you should possess in-depth knowledge of designing, developing, and overseeing analytical assets, including semantic models, data warehouses, and lakehouses.As you prepare for this certification, you'll gain the expertise needed to solve real-world challenges by mastering key Fabric components.LakehouseWarehouseEventhouse / KQL DatabaseSpark NotebookDataflowsSemantic modelReport As a candidate for this certification 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 Solution architectsData architects Data analysts, Data engineers, Data scientistsAI engineersadministrators.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).Practice set contains questions from all 3 below domains and once you attended a practice set, you can review where you will get the actual answers along with EXPLANATION and official/course resource link.Maintain 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 behaviorImplement incremental refresh for semantic models

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