Introduction to Microsoft Fabric

Topics

• Microsoft Fabric platform overview
• Analytics Engineer role and responsibilities
• Core workloads: Data Engineering, Warehouse, Real-Time Intelligence, Data Science, and Power BI
• Workspaces, capacities, items, and tenant settings

Explore Microsoft Fabric Analytics Data Stores

Topics

• OneLake fundamentals and unified storage
• Fabric data stores: lakehouses, warehouses, eventhouses, and semantic models
• Store selection for batch, structured, and real-time data
• Shortcuts, tables, files, and folders
• Workspace organization and data discovery

Implement Lakehouse Solutions in Microsoft Fabric

Topics

• Create and configure lakehouses
• Manage files, tables, and Delta Lake data
• Transform data with notebooks and Spark
• Query data with SQL analytics endpoints
• Optimize tables for performance and reliability

Implement Warehouse Solutions in Microsoft Fabric

Topics

• Create and manage Fabric warehouses
• Design relational analytics objects: tables, schemas, views, stored procedures
• Load and transform data with T-SQL, pipelines, and dataflows
• Tune warehouse queries and performance
• Secure objects with permissions and workspace roles

Ingest and Transform Data in Microsoft Fabric

Topics

• Orchestrate ingestion with Data Factory pipelines
• Build low-code transformations with Dataflows Gen2
• Connect to cloud, on-premises, and SaaS data sources
• Implement incremental refresh and reusable logic
• Monitor runs, troubleshoot failures, and validate quality

Design and Manage Semantic Models

Topics

• Design star schemas, relationships, and hierarchies
• Create DAX assets: measures, calculated columns, calculation groups
• Select storage modes: Import, DirectQuery, Composite, Direct Lake
• Optimize model performance and refresh
• Apply row-level, object-level, and model security

Develop Reports and Analytical Experiences

Topics

• Create Power BI reports from Fabric data
• Design report experiences: visuals, filters, slicers, drill-through, navigation
• Improve report performance across storage modes
• Use paginated reports, metrics, and scorecards
• Explore Q&A, insights, and Copilot-enabled analytics

Secure and Govern Analytics Assets in Microsoft Fabric

Topics

• Configure access and sharing: workspace roles, item permissions, endorsement, sensitivity labels
• Manage discovery, lineage, ownership, and certification
• Secure lakehouses, warehouses, models, and reports
• Apply tenant, capacity, and workspace controls
• Monitor usage, compliance, and audit readiness

Prepare AI-Ready Analytics Data in Microsoft Fabric

Topics

• Prepare clean, structured analytics data
• Organize medallion layers: bronze, silver, and gold data zones
• Apply quality checks, rules, and documentation
• Prepare models and datasets for AI-assisted analytics
• Improve metadata, access, and discoverability

Implement Real-Time Analytics and KQL in Fabric

Topics

• Real-Time Intelligence concepts
• Working with eventhouses and KQL databases
• Analyzing streaming data with KQL: explore, filter, aggregate, visualize events
• Connecting and transforming real-time sources
• Visualizing insights through dashboards and reports

Optimize Performance and Troubleshoot Analytics Solutions

Topics

• Identify performance bottlenecks across Fabric workloads
• Optimize Delta tables, SQL queries, Spark jobs, and refresh operations
• Tune semantic models, storage modes, and DAX
• Monitor capacity, workspace usage, and query performance
• Troubleshoot loading, transformation, security, and reporting issues

Manage Analytics Lifecycle and Deployment

Topics

• Plan dev, test, and production workspaces
• Promote Fabric items with deployment pipelines
• Use source control: Git integration, branching, teamwork practices
• Manage dependencies, lineage, and parameters
• Apply release governance, validation, and rollback planning

DP-600 Exam Preparation and Case Study Practice

Topics

• Review DP-600 domains and question patterns
• Solve scenario-based case studies: lakehouse, warehouse, semantic model, governance, reports
• Practice SQL, KQL, and DAX use cases
• Revise weak areas with labs and assessments
• Build a final exam readiness checklist

Hands-On Labs and Capstone Analytics Project

Topics

• Build an end-to-end Fabric analytics solution

Labs / Practice

• Ingest and transform data using pipelines, Dataflows Gen2, notebooks, and SQL
• Create lakehouse and warehouse assets
• Design models, measures, reports, and security rules
• Present governance, performance, and deployment choices