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所在平台: Udemy |
课程主页: https://www.udemy.com/course/validate-your-learning-microsoft-fabric-skills-assessment/
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课程名称：Fabric Analytics Engineer Associate DP-600 考试模拟测试 课程概述：该课程旨在帮助学员准备DP-600考试，即使用Microsoft Fabric实施分析解决方案。课程包含200道测试题及深入的解答解析，重点聚焦以下技能： 1. 数据分析解决方案的规划、实施和管理（10-15%） - 规划数据分析环境，识别解决方案需求。 - 推荐Fabric管理门户的设置和选择数据网关类型。 - 创建自定义Power BI报告主题，管理数据分析环境，实施版本控制等。 2. 数据的准备和提供（40-45%） - 在湖仓或仓库中创建对象，使用数据管道、数据流或笔记本进行数据摄取。 - 实施数据清洗过程，创建视图、函数和存储过程。 - 转换和优化数据，包括聚合、去重、数据类型转换等。 3. 语义模型的实施和管理（20-25%） - 设计和构建语义模型，选择存储模式，实施星型架构。 - 使用DAX编写计算，实施动态行级安全和对象级安全。 - 优化企业级语义模型和查询性能。 4. 数据的探索和分析（20-25%） - 进行探索性分析和描述性分析，集成预测分析到可视化或报告中。 - 使用SQL查询湖仓和仓库的数据，连接并查询数据集。 该课程通过理清理论基础和实践测试，帮助学员全面掌握使用Microsoft Fabric实施分析解决方案所需的技能，为通过DP-600考试做好充分准备。
Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric200 QUESTIONS WITH IN DEPTH EXPLANATIONS (PRACTICE Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric)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 endpoint
