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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ms_dp_600/
课程评论：没有评论
课程名称：Fabric Analytics Engineer (DP-600) 考试问题（2025年5月） 课程概述： 本课程主要旨在帮助学员掌握数据分析解决方案的维护、数据准备、语义模型的实施与管理等关键技能。课程内容包括以下几个主要模块： 1. **维护数据分析解决方案 (占比25-30%)** - 实施安全与治理措施 - 实施工作区级别和项级别的访问控制 - 应用敏感性标签及项目的背书 - 维护分析开发生命周期，配置工作区的版本控制 - 管理Power BI Desktop项目及其部署管道，进行影响分析，并管理与湖屋、数据仓库、数据流和语义模型的下游依赖关系 - 创建和更新可重用资产，如Power BI模板和数据源文件 2. **准备数据 (占比45-50%)** - 创建数据连接，利用OneLake数据中心发现和获取数据 - 数据摄取，选择合适的数据存储方案（湖屋、仓库或事件屋） - 数据转换，包括创建视图、存储过程以及丰富数据、实施星型模式、去规范化及数据聚合 - 识别和解决数据中的重复、缺失和空值，进行数据类型转换和过滤 3. **实施和管理语义模型 (占比25-30%)** - 设计与构建语义模型，选择存储模式，实施星型模式和关系配置 - 编写DAX计算，包括动态格式字符串和计算组 - 识别和优化企业级语义模型，实现查询和报告可视化的性能改进 - 配置Direct Lake和增量刷新功能，以提高语义模型的性能 本课程适合希望深入理解数据分析解决方案及其管理的专业人士。通过系统的学习，学员将具备必要的技术能力，为参加DP-600考试做好准备。
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 behaviorImplement incremental refresh for semantic models
