Introduction
Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric: Introduction
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Lesson 1: Introduction to Microsoft Fabric Data Engineering
Learning objectives
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Understanding Microsoft Fabric fundamentals
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Surveying the key components of Microsoft Fabric data engineering
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Understanding the role of data engineers in modern data ecosystems
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Comparing data engineering, data science, and analytics
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Accessing Microsoft Fabric
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Understanding required permissions and licensing
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Lesson 2: Configure Fabric Workspaces
Configuring Spark settings
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Configuring domain settings
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Configuring OneLake settings
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Configuring data workflow settings
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Quiz
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Lesson 3: Implement Lifecycle Management
Learning objectives
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Using Git to version control workspaces and items
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Utilizing database projects for warehouse
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Implementing deployment pipelines
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Quiz
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Lesson 4: Configure Security and Governance
Learning objectives
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Configuring access controls for workspaces
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Configuring access controls for items
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Configuring RLS, CLS, object-level, and folder/file-level access controls
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Configuring dynamic data masking
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Applying sensitivity labels and endorsing items
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Configuring and using workspace logging
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Configuring and using OneLake security
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Quiz
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Lesson 5: How to Orchestrate Fabric Items
Configuring pipeline schedules
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Configuring notebook schedules
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Configuring Dataflow Gen2 schedules
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Using parameters and dynamic expressions in pipelines
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Using parameters in notebooks
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Quiz
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Lesson 6: Design and Implement Loading Patterns
Implementing full data loads
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Implementing incremental data loads
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Preparing data for ingestion into a dimensional model
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Quiz
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Lesson 7: Ingest and Transform Batch Data
Learning objectives
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Choosing between a lakehouse and a warehouse for data storage
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Transforming data using Power Query, PySpark, KQL, and T-SQL
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Creating and managing lakehouse shortcuts
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Creating and managing mirroring
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Using pipelines to ingest data
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Ingesting data by using continuous integration from OneLake
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Designing a dimensional model
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Grouping and aggregating data
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Handling duplicate, missing, and late-arriving data
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Quiz
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Lesson 8: Ingest and Transform Streaming Data
Learning objectives
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Choosing between Eventstream, Spark Structured Streaming, and KQL for streaming
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Understanding KQL database native storage, followed storage, and OneLake shortcuts
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Using Eventstreams to process data
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Using Spark Structured Streaming to process data
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Using KQL to process data
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Using windowing functions to query streaming data
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Quiz
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Lesson 9: Monitor Fabric Items
Learning objectives
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Monitoring data ingestion
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Monitoring data transformation
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Monitoring Power BI semantic model refreshes
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Configuring alerts
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Quiz
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Lesson 10: How to Identify and Resolve Errors
Learning objectives
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Troubleshooting and resolving pipeline errors
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Troubleshooting and resolving dataflow errors
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Troubleshooting and resolving notebook errors
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Troubleshooting and resolving Eventhouse and Eventstream errors
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Troubleshooting and resolving T-SQL errors
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Troubleshooting and resolving Shortcut errors
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Quiz
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Lesson 11: How to Optimize Performance
Learning objectives
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Optimizing a lakehouse table
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Optimizing a data factory pipeline
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Optimizing a warehouse
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Optimizing Eventstreams and Eventhouses
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Optimizing Spark performance
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Optimizing query performance
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Quiz
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Lesson 12: Practical Applications and Case Studies
Learning objectives
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Surveying industry-specific examples (finance, healthcare, retail)
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Exploring success stories using Microsoft Fabric
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Applying lessons learned and best practices
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Exploring guided mini-projects
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Summary
Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric: Summary
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