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所在平台: Udemy |
课程主页: https://www.udemy.com/course/dp-203-microsoft-azure-data-engineer-practice-exam-wlabs/
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课程名称:DP-203:Microsoft Azure 数据工程师考试与实验 课程概述: DP-203:Microsoft Azure 数据工程师助理实践考试是一款全面而有效的工具,旨在帮助您准备 Microsoft Azure 数据工程师助理认证考试。此实践考试由行业专家精心设计,涵盖了通过 DP-203 考试所需的所有核心主题和技能。考试结构与实际考试格式高度相似,让您可以熟悉考试环境和题型,从而在考试当天建立信心,减轻焦虑,确保您发挥最佳水平。 课程特点: 1. **全面覆盖考试目标**:从设计和实施数据存储解决方案到数据处理解决方案的设计和实施,所有关键领域均有涉及。 2. **丰富的实践题目**:题目设置模拟了实际考试的难度和格式,助您做好充分准备。 3. **详细的解题方案**:每个问题均配有详细解释,帮助您理解正确答案背后的推理,识别弱点并从错误中学习,以提升整体表现。 考试信息: - 考试名称:Microsoft Certified - Azure Data Engineer Associate - 考试代码:DP-203 - 考试费用:165 美元 - 可用语言:英语、日语、韩语和简体中文 - 考试格式:多项选择题 - 题目数量:约 40-60 道 - 考试时长:150 分钟 - 通过分数:700-1000 课程内容大纲: 1. **设计和实施数据存储 (40-45%)**:设计数据存储结构、实现数据冗余、构建时间序列数据解决方案等。 2. **设计和开发数据处理 (25-30%)**:数据的摄取与转换、批处理和流处理的解决方案开发、数据管道的设计与实施等。 3. **设计和实施数据安全 (10-15%)**:数据政策与标准的设计、数据加密、数据审计策略等。 4. **监控和优化数据存储与数据处理 (10-15%)**:实施日志记录、监控数据管道性能、优化资源管理等。 无论您是希望验证技能的资深数据工程师,还是希望开创职业生涯的新手,DP-203:Microsoft Azure 数据工程师助理实践考试都是您实现目标的最佳工具。凭借其全面的覆盖、逼真的实践题目和详细的解释,这项实践考试将为您在 DP-203 考试中取得成功提供所需的知识和信心。不要把成功留给偶然——今天就投资于 DP-203:Microsoft Azure 数据工程师助理实践考试,迈出成为认证 Microsoft Azure 数据工程师助理的第一步。
DP-203: Microsoft Azure Data Engineer Associate Practice Exam, a comprehensive and effective tool designed to help you prepare for the Microsoft Azure Data Engineer Associate certification exam. This practice exam is meticulously crafted by industry experts and covers all the essential topics and skills required to pass the DP-203 exam with flying colors.DP-203: Microsoft Azure Data Engineer Associate Practice Exam is structured in a manner that closely mirrors the actual exam format, allowing you to familiarize yourself with the exam environment and question types. This will help you build confidence and reduce test anxiety on the day of the exam, ensuring that you can perform at your best.One of the key features of the DP-203: Microsoft Azure Data Engineer Associate Practice Exam is its comprehensive coverage of all the exam objectives. From designing and implementing data storage solutions to designing and implementing data processing solutions, this practice exam covers all the key areas that you need to master in order to pass the DP-203 exam.In addition to covering the exam objectives, the DP-203: Microsoft Azure Data Engineer Associate Practice Exam also includes a wide range of practice questions that are designed to test your knowledge and understanding of the material. These questions are carefully crafted to mimic the difficulty level and format of the actual exam questions, ensuring that you are well-prepared for any challenges that may arise during the exam.Furthermore, the DP-203: Microsoft Azure Data Engineer Associate Practice Exam includes detailed explanations for each question, allowing you to understand the reasoning behind the correct answers. This will not only help you identify your areas of weakness but also enable you to learn from your mistakes and improve your overall performance.DP-203: Microsoft Azure Data Engineer Exam Summary:Exam Name: Microsoft Certified - Azure Data Engineer AssociateExam code: DP-203Exam voucher cost: $165 USDExam languages: English, Japanese, Korean, and Simplified ChineseExam format: Multiple-choice, multiple-answerNumber of questions: 40-60 (estimate)Length of exam: 150 minutesPassing grade: Score is from 700-1000.DP-203: Microsoft Azure Data Engineer Syllabus::Design and implement data storage (40-45%)Design a data storage structureDesign an Azure Data Lake solutionRecommend file types for storageRecommend file types for analytical queriesDesign for efficient queryingDesign for data pruningDesign a folder structure that represents the levels of data transformationDesign a distribution strategyDesign a data archiving solutionDesign a partition strategyDesign a partition strategy for filesDesign a partition strategy for analytical workloadsDesign a partition strategy for efficiency/performanceDesign a partition strategy for Azure Synapse AnalyticsIdentify when partitioning is needed in Azure Data Lake Storage Gen2Design the serving layerDesign star schemasDesign slowly changing dimensionsDesign a dimensional hierarchyDesign a solution for temporal dataDesign for incremental loadingDesign analytical storesDesign metastores in Azure Synapse Analytics and Azure DatabricksImplement physical data storage structuresImplement compressionImplement partitioning Implement shardingImplement different table geometries with Azure Synapse Analytics poolsImplement data redundancyImplement distributionsImplement data archivingImplement logical data structuresBuild a temporal data solutionBuild a slowly changing dimensionBuild a logical folder structureBuild external tablesImplement file and folder structures for efficient querying and data pruningImplement the serving layerDeliver data in a relational starDeliver data in Parquet filesMaintain metadataImplement a dimensional hierarchyDesign and develop data processing (25-30%)Ingest and transform dataTransform data by using Apache SparkTransform data by using Transact-SQLTransform data by using Data FactoryTransform data by using Azure Synapse PipelinesTransform data by using Stream AnalyticsCleanse dataSplit dataShred JSONEncode and decode dataConfigure error handling for the transformationNormalize and denormalize valuesTransform data by using ScalaPerform data exploratory analysisDesign and develop a batch processing solutionDevelop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure DatabricksCreate data pipelinesDesign and implement incremental data loadsDesign and develop slowly changing dimensionsHandle security and compliance requirementsScale resourcesConfigure the batch sizeDesign and create tests for data pipelinesIntegrate Jupyter/Python notebooks into a data pipelineHandle duplicate dataHandle missing dataHandle late-arriving dataUpsert dataRegress to a previous stateDesign and configure exception handlingConfigure batch retentionDesign a batch processing solutionDebug Spark jobs by using the Spark UIDesign and develop a stream processing solutionDevelop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event HubsProcess data by using Spark structured streamingMonitor for performance and functional regressionsDesign and create windowed aggregatesHandle schema driftProcess time series dataProcess across partitionsProcess within one partitionConfigure checkpoints/watermarking during processingScale resourcesDesign and create tests for data pipelinesOptimize pipelines for analytical or transactional purposesHandle interruptionsDesign and configure exception handlingUpsert dataReplay archived stream dataDesign a stream processing solutionManage batches and pipelinesTrigger batchesHandle failed batch loadsValidate batch loadsManage data pipelines in Data Factory/Synapse PipelinesSchedule data pipelines in Data Factory/Synapse PipelinesImplement version control for pipeline artifactsManage Spark jobs in a pipelineDesign and implement data security (10-15%)Design security for data policies and standardsDesign data encryption for data at rest and in transitDesign a data auditing strategyDesign a data masking strategyDesign for data privacyDesign a data retention policyDesign to purge data based on business requirementsDesign Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2Design row-level and column-level securityImplement data securityImplement data maskingEncrypt data at rest and in motionImplement row-level and column-level securityImplement Azure RBACImplement POSIX-like ACLs for Data Lake Storage Gen2Implement a data retention policyImplement a data auditing strategyManage identities, keys, and secrets across different data platform technologiesImplement secure endpoints (private and public)Implement resource tokens in Azure DatabricksLoad a DataFrame with sensitive informationWrite encrypted data to tables or Parquet filesManage sensitive informationMonitor and optimize data storage and data processing (10-15%)Monitor data storage and data processingImplement logging used by Azure MonitorConfigure monitoring servicesMeasure performance of data movementMonitor and update statistics about data across a systemMonitor data pipeline performanceMeasure query performanceMonitor cluster performanceUnderstand custom logging optionsSchedule and monitor pipeline testsInterpret Azure Monitor metrics and logsInterpret a Spark directed acyclic graph (DAG)Optimize and troubleshoot data storage and data processingCompact small filesRewrite user-defined functions (UDFs)Handle skew in dataHandle data spillTune shuffle partitionsFind shuffling in a pipelineOptimize resource managementTune queries by using indexersTune queries by using cacheOptimize pipelines for analytical or transactional purposesOptimize pipeline for descriptive versus analytical workloadsTroubleshoot a failed spark jobTroubleshoot a failed pipeline runWhether you are a seasoned data engineer looking to validate your skills or a newcomer to the field aiming to kick-start your career, the DP-203: Microsoft Azure Data Engineer Associate Practice Exam is the perfect tool to help you achieve your goals. With its comprehensive coverage, realistic practice questions, and detailed explanations, this practice exam will equip you with the knowledge and confidence you need to succeed on the DP-203 exam.Don't leave your success to chance - invest in the DP-203: Microsoft Azure Data Engineer Associate Practice Exam today and take the first step towards becoming a certified Microsoft Azure Data Engineer Associate.