By completing this course, participants will be able to:

• Design and implement enterprise-scale analytics solutions using Microsoft Fabric.
• Work with Fabric lakehouses, warehouses, and eventhouses.
• Prepare and transform data using dataflows, notebooks, and T-SQL.
• Design dimensional models and implement effective data structures.
• Build and manage enterprise-scale semantic models.
• Develop calculations using DAX and optimize semantic-model performance.
• Work with Direct Lake and understand its use in enterprise analytics.
• Implement relationships, star schemas, composite models, and other semantic-model structures.
• Configure security and governance across Fabric analytics assets.
• Manage analytics development using version control and deployment pipelines.
• Perform impact analysis across dependent analytics assets.
• Apply sensitivity labels and item-level security controls.
• Optimize queries, reports, DAX, and semantic models.
• Prepare analytics data for AI and downstream consumers.
• Prepare for the Microsoft Certified: Fabric Analytics Engineer Associate certification.

Module 1: Explore Analytics Data Stores in Microsoft Fabric

• Introduction to Microsoft Fabric analytics
• OneLake and Fabric data architecture
• Lakehouses
• Fabric data warehouses
• Eventhouses and real-time analytics
• Selecting appropriate Fabric data stores
• Working with analytics data in Fabric

Module 2: Design and Transform Analytics Data in Microsoft Fabric

• Data ingestion and transformation
• Dataflows
• Data pipelines
• Fabric notebooks
• T-SQL for analytics workloads
• Data transformation strategies
• Designing dimensional data structures
• Working with lakehouses and warehouses
• Loading and transforming analytical data

Module 3: Design and Manage Semantic Models in Microsoft Fabric

• Semantic-model architecture
• Star-schema design
• Fact and dimension tables
• Relationships
• Many-to-many relationships
• Bridge tables
• DAX calculations
• Measures and calculated logic
• Calculation groups
• Dynamic format strings
• Field parameters
• Composite models
• Large semantic models
• Direct Lake
• Semantic-model optimization

Module 4: Prepare AI-Ready Analytics Data in Microsoft Fabric

• Preparing analytical data for AI consumers
• Designing data structures for AI workloads
• Preparing and enriching data
• Making analytics data accessible to AI agents
• Supporting AI-ready data scenarios
• Applying appropriate governance to AI-consumed data

Module 5: Secure and Govern Analytics Data in Microsoft Fabric

• Fabric workspace security
• Item-level access controls
• Row-level security
• Column-level security
• Object-level security
• File-level access controls
• Sensitivity labels
• Data endorsement
• Governance practices
• Version control
• Power BI Desktop projects
• Deployment pipelines
• Impact analysis
• XMLA endpoint
• Reusable analytics assets

Module 6: Optimize Enterprise-Scale Analytics Solutions

• Query performance
• Report and visual performance
• DAX performance optimization
• Direct Lake configuration
• Direct Lake fallback behavior
• Choosing Direct Lake on OneLake versus the SQL analytics endpoint
• Incremental refresh
• Large-scale semantic-model management
• Enterprise analytics optimization

4 Days

• Creating and configuring Fabric workspaces.
• Working with OneLake and Fabric data stores.
• Creating and configuring lakehouses.
• Working with Fabric warehouses.
• Working with eventhouses and analytical data.
• Building data ingestion and transformation workflows.
• Using dataflows and pipelines.
• Using notebooks for data preparation and transformation.
• Querying data using T-SQL.
• Designing dimensional models.
• Building star schemas.
• Creating and managing semantic models.
• Implementing relationships and many-to-many relationships.
• Creating DAX measures and calculations.
• Working with calculation groups and field parameters.
• Configuring Direct Lake.
• Optimizing semantic-model performance.
• Implementing security and governance.
• Configuring deployment pipelines and development lifecycle controls.
• Applying sensitivity labels and access controls.
• Performing impact analysis across analytics assets.

Certification: Microsoft Certified: Fabric Analytics Engineer Associate

Duration: 100 minutes

Format: 40–60 questions

• Fabric Analytics Engineers
• Data Analysts
• Data Engineers
• BI Developers
• Analytics Engineers
• Power BI Professionals
• Data Modelers
• Business Intelligence Professionals
• Data Platform Professionals
• Professionals responsible for enterprise analytics solutions

• Data modeling.
• Data transformation.
• Data analytics.
• Translating business requirements into analytical measures.
• SQL or DAX.
• Building semantic models and Power BI reports.