Overview

Welcome to the definitive course for mastering Microsoft Azure DP-203 Data Engineering! The DP-203 exam, with its passing score of 70, a tight 120-minute time limit, and 60 questions, poses a significant challenge. But fear not, as this course is your key to success.This Practice Exam includes an in-depth exam guide designed to ensure your success. We provide you with five meticulously crafted practice tests, each simulating the real exam experience. These tests are your preparation pathway, specifically designed to help you tackle the unique challenges posed by NB (non-binary) questions.Data Engineering on Microsoft Azure Exam Summary:Number of Questions: Maximum of 40-60 questions,Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,Length of Test: 150 Minutes. The exam is available in English and Japanese languages.Passing Score: 700 / 1000Languages: English at launch. JapaneseSchedule Exam: Pearson VUEMicrosoft Azure DP-203 Data Engineering Syllabus::Design and implement data storage (15-20%)Implement a partition strategyImplement a partition strategy for filesImplement a partition strategy for analytical workloadsImplement a partition strategy for streaming workloadsImplement a partition strategy for Azure Synapse AnalyticsIdentify when partitioning is needed in Azure Data Lake Storage Gen2Design and implement the data exploration layerCreate and execute queries by using a compute solution that leverages SQL serverless and Spark clusterRecommend and implement Azure Synapse Analytics database templatesPush new or updated data lineage to Microsoft PurviewBrowse and search metadata in Microsoft Purview Data CatalogDevelop data processing (40-45%)Ingest and transform dataDesign and implement incremental loadsTransform data by using Apache SparkTransform data by using Transact-SQL (T-SQL) in Azure Synapse AnalyticsIngest and transform data by using Azure Synapse Pipelines or Azure Data FactoryTransform data by using Azure Stream AnalyticsCleanse dataHandle duplicate dataAvoiding duplicate data by using Azure Stream Analytics Exactly Once DeliveryHandle missing dataHandle late-arriving dataSplit dataShred JSONEncode and decode dataConfigure error handling for a transformationNormalize and denormalize dataPerform data exploratory analysisDevelop a batch processing solutionDevelop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data FactoryUse PolyBase to load data to a SQL poolImplement Azure Synapse Link and query the replicated dataCreate data pipelinesScale resourcesConfigure the batch sizeCreate tests for data pipelinesIntegrate Jupyter or Python notebooks into a data pipelineUpsert dataRevert data to a previous stateConfigure exception handlingConfigure batch retentionRead from and write to a delta lakeDevelop a stream processing solutionCreate a stream processing solution by using Stream Analytics and Azure Event HubsProcess data by using Spark structured streamingCreate windowed aggregatesHandle schema driftProcess time series dataProcess data across partitionsProcess within one partitionConfigure checkpoints and watermarking during processingScale resourcesCreate tests for data pipelinesOptimize pipelines for analytical or transactional purposesHandle interruptionsConfigure exception handlingUpsert dataReplay archived stream dataManage batches and pipelinesTrigger batchesHandle failed batch loadsValidate batch loadsManage data pipelines in Azure Data Factory or Azure Synapse PipelinesSchedule data pipelines in Data Factory or Azure Synapse PipelinesImplement version control for pipeline artifactsManage Spark jobs in a pipelineSecure, monitor, and optimize data storage and data processing (30-35%)Implement data securityImplement data maskingEncrypt data at rest and in motionImplement row-level and column-level securityImplement Azure role-based access control (RBAC)Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2Implement a data retention policyImplement 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 data storage and data processingImplement logging used by Azure MonitorConfigure monitoring servicesMonitor stream processingMeasure performance of data movementMonitor and update statistics about data across a systemMonitor data pipeline performanceMeasure query performanceSchedule and monitor pipeline testsInterpret Azure Monitor metrics and logsImplement a pipeline alert strategyOptimize and troubleshoot data storage and data processingCompact small filesHandle skew in dataHandle data spillOptimize resource managementTune queries by using indexersTune queries by using cacheTroubleshoot a failed Spark jobTroubleshoot a failed pipeline run, including activities executed in external servicesUpon completing this course, you'll acquire the skills and knowledge required to successfully pass the DP-203 exam and earn your certification in data engineering on Microsoft Azure. You'll gain proficiency in handling real-world data engineering challenges, master the art of data pipelines, optimize data storage, and orchestrate data effectively.This course can help you prepare for Microsoft Azure DP-203 Data Engineering exam. This is the final course in a four-course program that prepares you to take the DP-203 certification exam. This course gives you opportunities to hone your exam technique and refresh your knowledge of all the key areas assessed in the certification exam.

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