DP–700 Certification Guide Data Engineering Solutions with Microsoft Fabric

Syllabus Outline
Chapter 1: Introduction to Microsoft FabricIntroduction to DP-700Overview of Microsoft FabricKey components of Microsoft FabricPrerequisites for the DP-700 ExamPreparing for the ExamCareer and Job OpportunitiesChapter 2: Planning and Designing Data Engineering SolutionsMedallion architecture principlesData modeling best practicesChoosing appropriate data storage solutions (e.g., Azure Data Lake, Delta Lake)Real-time vs. batch data processing considerationsChapter 3: Implementing Data IngestionBatch data ingestion using pipelines and dataflowsStreaming data ingestion using EventStream and Spark Structured StreamingHandling different ingestion patterns: full load, incremental load, and mirroringData quality and duplication managementChapter 4: Transforming Data in Microsoft FabricData transformation tools: PySpark, T-SQL, and KQLDelta Lake API: Optimizations, VACUUM, and V-OrderDenormalization and aggregation techniquesHandling late-arriving and missing dataChapter 5: Orchestrating Data PipelinesCreating pipelines with triggers and schedulesOrchestration with notebooks and parameterized workflowsIntegration of pipelines with Git for version controlDynamic data flow patternsError handling and debugging in pipelinesChapter 6: Securing Data SolutionsWorkspace and item-level access controlsRow-level, column-level, and object-level securityDynamic data maskingSensitivity labels and data governance practicesChapter 7: Real-Time IntelligenceProcessing and querying real-time dataImplementing windowing functionsMonitoring real-time data flows with EventStream and KQLBuilding and optimizing real-time analytics solutionsChapter 8: Monitoring and Optimizing Data SolutionsMonitoring data pipelines and transformationsResolving errors in pipelines, dataflows, and notebooksPerformance optimization for data ingestion and transformationsConfiguring alerts and real-time monitoringChapter 9: Deploying and Managing SolutionsUsing deployment pipelines in Microsoft FabricMulti-environment deployment strategiesManaging semantic models and data warehousesBest practices for deployment and scalingChapter 10: Practice Questions and Exam PreparationPractice MCQs with detailed explanationsCase-study-based scenariosTips for exam successKey Features of This Book:Planning and Designing Data SolutionsBatch and Streaming Data IngestionData TransformationSecuring DataReal-Time AnalyticsExam Preparation