| Domain 1: Plan, implement, and manage data engineering solutions | 30–35% | Configure Spark settings, pools, and environment libraries | Use Apache Spark in Microsoft Fabric | Lab 02 | 🟢 Complete |
| | Design Lakehouse vs Warehouse architectures | Get Started with Lakehouses in Microsoft Fabric | Lab 01 | 🟢 Complete |
| | Manage OneLake shortcuts (ADLS, S3, GCS, Dataverse) | Configure OneLake Shortcuts and Data Sharing | Lab 01 | 🟢 Complete |
| | Implement lifecycle management (Git integration, deployment pipelines) | Plan, Implement, and Manage Data Engineering Solutions | Lab 07 | 🟢 Complete |
| | Configure Apache Airflow workspaces and DAGs | Implement Data Pipelines with Apache Airflow in Fabric | Lab 06 | 🟢 Complete |
| | Implement workspace roles and item-level permissions | Implement Security and Governance for Fabric Data Engineering | Lab 08 | 🟢 Complete |
| | Implement RLS, CLS, OLS, and Dynamic Data Masking | Implement Security and Governance for Fabric Data Engineering | Lab 08 | 🟢 Complete |
| | Manage Purview sensitivity labels & private endpoints | Implement Security and Governance for Fabric Data Engineering | Lab 08 | 🟢 Complete |
| Domain 2: Ingest and transform data | 30–35% | Ingest data via Data Factory pipelines & CDC | Use Data Factory Pipelines in Microsoft Fabric | Lab 04 | 🟢 Complete |
| | Transform data with Dataflows Gen2 & Fast Copy | Ingest Data with Dataflows Gen2 in Microsoft Fabric | Lab 05 | 🟢 Complete |
| | Ingest streaming data via Eventstreams & Eventhouses | Ingest Real-Time Data with Eventstreams in Microsoft Fabric | Lab 03 | 🟢 Complete |
| | Implement Database Mirroring (Azure SQL, Cosmos, Snowflake) | Mirror Databases in Microsoft Fabric | Lab 04 | 🟢 Complete |
| | Implement Medallion architecture (Bronze, Silver, Gold) | Organize a Fabric Lakehouse using Medallion Architecture Design | Lab 01 | 🟢 Complete |
| | Transform data with PySpark DataFrames & Spark SQL | Use Apache Spark in Microsoft Fabric | Lab 02 | 🟢 Complete |
| | Implement PySpark Structured Streaming & Windowing | Use Apache Spark in Microsoft Fabric | Lab 02 | 🟢 Complete |
| | Optimize Delta tables (OPTIMIZE, V-ORDER, VACUUM, Liquid Clustering) | Work with Delta Lake Tables in Microsoft Fabric | Lab 02 | 🟢 Complete |
| | Author KQL queries, update policies, and materialized views | Query and Transform Data in KQL Databases | Lab 03 | 🟢 Complete |
| Domain 3: Monitor and optimize data engineering solutions | 30–35% | Monitor Fabric Capacity Metrics app (CU, bursting, smoothing) | Manage Capacity and Performance in Microsoft Fabric | Lab 09 | 🟢 Complete |
| | Monitor Spark applications, stages, and executor logs | Monitor and Optimize Data Engineering Workloads in Fabric | Lab 10 | 🟢 Complete |
| | Detect and resolve Spark data skew and spill-to-disk | Monitor and Optimize Data Engineering Workloads in Fabric | Lab 10 | 🟢 Complete |
| | Troubleshoot pipeline failures and capacity throttling | Manage Capacity and Performance in Microsoft Fabric | Lab 09 | 🟢 Complete |
