Free Udemy Course: Practice Exams | MS Azure DP-700 Data Engineering Solutions

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.As a candidate for this exam, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes. Your responsibilities for this role include:Ingesting and transforming data.Securing and managing an analytics solution.Monitoring and optimizing an analytics solution.You work closely with analytics engineers, architects, analysts, and administrators to design and deploy data engineering solutions for analytics.You should be skilled at manipulating and transforming data by using Structured Query Language (SQL), PySpark, and Kusto Query Language (KQL).Skills at a glanceImplement and manage an analytics solution (30–35%)Ingest and transform data (30–35%)Monitor and optimize an analytics solution (30–35%)Implement and manage an analytics solution (30–35%)Configure Microsoft Fabric workspace settingsConfigure Spark workspace settingsConfigure domain workspace settingsConfigure OneLake workspace settingsConfigure data workflow workspace settingsImplement lifecycle management in FabricConfigure version controlImplement database projectsCreate and configure deployment pipelinesConfigure security and governanceImplement workspace-level access controlsImplement item-level access controlsImplement row-level, column-level, object-level, and folder/file-level access controlsImplement dynamic data maskingApply sensitivity labels to itemsEndorse itemsImplement and use workspace loggingOrchestrate processesChoose between a pipeline and a notebookDesign and implement schedules and event-based triggersImplement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressionsIngest and transform data (30–35%)Design and implement loading patternsDesign and implement full and incremental data loadsPrepare data for loading into a dimensional modelDesign and implement a loading pattern for streaming dataIngest and transform batch dataChoose an appropriate data storeChoose between dataflows, notebooks, KQL, and T-SQL for data transformationCreate and manage shortcuts to dataImplement mirroringIngest data by using pipelinesTransform data by using PySpark, SQL, and KQLDenormalize dataGroup and aggregate dataHandle duplicate, missing, and late-arriving dataIngest and transform streaming dataChoose an appropriate streaming engineChoose between native storage, followed storage, or shortcuts in Real-Time IntelligenceProcess data by using eventstreamsProcess data by using Spark structured streamingProcess data by using KQLCreate windowing functionsMonitor and optimize an analytics solution (30–35%)Monitor Fabric itemsMonitor data ingestionMonitor data transformationMonitor semantic model refreshConfigure alertsIdentify and resolve errorsIdentify and resolve pipeline errorsIdentify and resolve dataflow errorsIdentify and resolve notebook errorsIdentify and resolve eventhouse errorsIdentify and resolve eventstream errorsIdentify and resolve T-SQL errorsOptimize performanceOptimize a lakehouse tableOptimize a pipelineOptimize a data warehouseOptimize eventstreams and eventhousesOptimize Spark performanceOptimize query performance