Overview

PRACTICE - PRACTICE - PRACTICE: PRACTICE WILL MAKE YOU PERFECT & become Microsoft Certified: Fabric Data Engineer AssociateCourse provides several practice sets similar to actual exam questions as per official exam syllabus and study guide for Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric.Practice set contains questions from all 3 below domains and once you attended a practice set, you can review where you will get the actual answers along with EXPLANATION and official/course resource link.This EXAM DP-700 contains 3 sections/domains having equal priority:Implement and manage an analytics solution (30-35%)Ingest and transform data (30-35%)Monitor and optimize an analytics solution (30-35%)Recommendation is to attend following EXAM DP-700 preparation course for better and effective preparation "Exam DP-700: Fabric Data Engineer Associate - Ultimate Guide" as it provides syllabus wise concept class followed by related hands-on Lab/demo session immediately to visualize how to use or implement it for effective learning.IMPORTANT NOTE: As per Official update from Microsoft, "Microsoft Certified: Azure Data Engineer Associate Certification and its related Exam DP-203: Data Engineering on Microsoft Azure will all be retired on March 31, 2025."So Exam DP-700 & Certification is VERY IMPORTANT as it is future of Data Engineering certification from Microsoft: DP-700: Microsoft Fabric Data Engineer AssociateWhat you will learn from this course? As per official syllabus and study guide, Practice sets provides questions including below questions ( with actual answers along with EXPLANATION and official/course resource link); You can get concept/theory/fundamental followed by DEMO session for each the below questions from preparation course "Exam DP-700: Fabric Data Engineer Associate - Ultimate Guide" available from this instructor.Fabric components/domain/concept/language part of this practice set: Lakehouse WarehouseReal time Hub / Real-Time IntelligencedataflowsNotebookSpark, PySpark, SQL, KQL (Kusto Query Language)Data Factory PipelineEventhouseEventstreamFor Configure Spark workspace settings: What is Starter Pool ?How to modify Starter Pool in Fabric and how its related Fabric settings impacts Starter Pool?What is custom Spark Pool? How to create custom Spark pool in Fabric and how its related settings impacts custom Spark pool What is Environment and what are their features and related settings that affects these.How to create Environment and how its related settings impacts compute configuration of Environment.What is the impact when these pool/environment are made as default pool in workspace.For Configure security and governance in Fabric, What is workspace-level access controls? Overview & ConceptHow to implement workspace-level access controls in Fabric through Hands on Lab/Demo.What is item-level access controls? Overview & ConceptHow to implement item-level access controls in Fabric through Hands on Lab/Demo.What is file-level access controls? Overview & ConceptHow to implement file-level access controls in Fabric through Hands on Lab/Demo.What is object-level access controls? Overview & ConceptHow to implement object-level access controls in Fabric through Hands on Lab/Demo.What is row-level access controls? Overview & Concept - Row Level Security (RLS)How to implement row-level access controls in Fabric through Hands on Lab/Demo.What is column-level access controls? Overview & Concept - Column Level Security (CLS)How to implement column-level access controls in Fabric through Hands on Lab/Demo.What is dynamic data masking in Fabric? Overview & Concept How to implement dynamic data masking in Fabric through Hands on Lab/Demo. For Transform data by using KQL (Ingest and transform batch data - Part 5) KQL Fundamentals: Query Operator & PipeKQL Fundamentals & Hands on Lab: Query Operator - Project , count, getschemaHow to translate SQL query to KQL QueryHow to find relevant data using distinct, take operator, Let statement in KQL How to find relevant data using Filter/Where in KQL How to find relevant data using Case (like if/then/elseif ) in KQL How to use KQL SearchHow to implement sorting records using Sort operatorHow to returns first N rows using top operator in KQL How to Create Columns using Extend operator in KQL How to Keep/Remove/Reorder Columns using KQL Project operators - project, project-away , project-keep,project-reorder, project-renameKQL join & best performanceHow to implement left right outer, Left semi join, Left anti join, Right semi join, Right anti join,full outer join in KQLHow to use summarize operator to perform Aggregation in KQLHow to perform Aggregation using KQL Aggregation functions Count() ,Countif(), sum() , sumif(), avg(), avgif() ,max(), maxif() ,min(), minif() How to perform KQL Aggregation (Group and aggregate data) - summarize by (Group and aggregate data:) - single aggregation, multiple aggregation (GROUP BY)For Transform data by using PySpark (Ingest and transform batch data - Part 3)How to use or implement select take using PySpark in Fabric How to implement Filter/Where transformation PySpark to clean dataHow to implement Drop, distinct, printschema using PySpark How to implement Sort()/OrderBy() to sort records using PySpark How to implement WithColumn, ColumnRenamed transformation using PySpark How to implement joins using PySpark How to implement Aggregations using PySpark How to implement Group and aggregate data using PySpark For Process data by using eventstreams (Ingest and transform streaming data)How to perform Manage fields transformation in eventstreamsHow to perform filter transformation in eventstreamsHow to perform aggregation transformation in eventstreamsHow to perform group by transformation using tumbling window in eventstreams How to perform group by transformation using hopping window in eventstreams How to perform group by transformation using sliding window in eventstreams How to perform Expand transformation in eventstreamsHow to perform union transformation in eventstreamsHow to perform join transformation in eventstreamsFor Configure version control, you will learn What is Version control? Concept & Integration ProcessWhat are related Fabric/Git permission and settings (and Tenant settings ) required to configure version control in Fabric?Hands-on Lab/Demo: How to set up Azure Repo that to be used as part of version control configuration.Hands-on Lab/Demo: How to configure version control/ Git integration with Azure Repo from Fabric workspaceFor Implement database projects, What is Database projects - concept & overviewDatabase projects - Setup & Architecture for demoWhy we need SQL database projects?Hands-on Lab/Demo: How to implement database projects in FabricFor Create and configure deployment pipelinesWhat is deployment pipeline in Fabric? overviewArchitecture of deployment pipeline for Demo & prerequisitesHands on Lab/Demo:How to Create and configure deployment pipelines Hands on Lab/Demo:How to assign workspace to respective stages and deploy content from one stage to next stage.For Configure domain workspace settings What is domain in Fabric ?What are the Delegated Setting for domainHands on Lab/Demo: how these delegated settings impacts domain in FabricFor SQL database projects:We will understand why we need SQL database projectsHow to Setup Demo components & understand through Architecture diagramHands on Lab/Demo - How to Implement database projects in FabricFor Configure security and governance in Fabric, What is sensitivity labels? Overview & ConceptSensitivity labels: related admin settings in FabricHow to apply sensitivity labels to items in Fabric?For Orchestrate processes, How to Choose between a pipeline and a notebook in Fabric?Design and implement schedules triggers - Design components for demoHow to implement schedules triggers in Fabric Data Factory pipeline.For Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressionData Factory pipeline good practiceWhat is Pipeline parameter and dynamic expression conceptHow to implement parameters and dynamic expression in pipelineHow to configure pipeline to retry if pipeline run failsHow to implement orchestration patterns with notebooks and pipelinesFor Design and implement loading patterns (Ingest and transform data), you will learnHow to design full and incremental data loads in FabricHow to implement full and incremental data loads in Fabric through hands-on lab/demoFor Ingest and transform batch data - part 1, you will learnhow to choose an appropriate data storehow to choose between dataflows, notebooks, and T-SQL for data transformationShortcuts overview in fabricShortcuts type in FabricShortcuts folder structureHow to create and manage shortcuts to data in Fabric through hands-on lab/demoFor Ingest data by using pipelines (Ingest and transform batch data - Part 2), you will learnHow to design Ingest data by using pipelines into LakehouseHow to ingest data by using pipelines into LakehouseHow to design Ingest data by using pipelines into warehouseHow to ingest data by using pipelines into warehouseHow to design Ingest data by using pipelines into KQL DatabaseHow to ingest data by using pipelines into KQL DatabaseFor Transform data by using SQL (Ingest and transform batch data - Part 4),How to implement SQL top distinct keyword How to implement SQL Filter on dataHow to implement SQL Sort on dataHow to implement Case & create dynamic or computed columnHow to implement SQL Inner Join, left Join, right Join, outer JoinHow to implement Aggregation in SQLHow to implement SQL Group and aggregate data: Group by & Having Clause AggregationHow to create Create Stored ProcedureHow to transform the data using Stored Procedure activity in Data pipeline For Optimize a lakehouse table (Optimize performance - Part 1) How to optimize a lakehouse table using Optimize command in FabricHow to optimize a lakehouse table using V-Order in FabricHow to optimize a lakehouse table using VACUUM command in FabricHow to optimize a lakehouse table using Optimizetwrite command in FabricHow to optimize a lakehouse table using Partition in FabricHow to optimize a lakehouse table using Table maintenance feature in Fabric Get the answer of the questions like: You have a Fabric workspace that contains a semantic model named Model1. You need to dynamically execute and monitor the refresh progress of Model1. What should you use?Study Guide for Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric: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

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