Master your DP-600T00 interview with these top 25 questions and expert answers to help you land your dream role as a Microsoft Fabric Analytics Engineer.
Microsoft Fabric is an end-to-end analytics platform that integrates various services like Power BI, Data Factory, Synapse, and Azure Data Lake into a unified SaaS experience. It provides a centralized environment for data ingestion, transformation, storage, and visualization. Power BI is embedded within Fabric to provide advanced reporting and dashboarding capabilities. Fabric’s OneLake simplifies data management by acting as a single logical data lake for all analytics workloads.
A Lakehouse combines features of data lakes and data warehouses, offering both structured and unstructured data capabilities. In Microsoft Fabric, the Lakehouse enables analysts and engineers to work with large volumes of data using a common storage format (like Delta Parquet) and offers native support for SQL, Spark, and T-SQL. It allows data modeling directly on top of data stored in OneLake, making real-time analytics and simplified data governance possible.
A Microsoft Fabric Analytics Engineer is responsible for designing, implementing, and maintaining data analytics solutions using Fabric tools like Synapse, Power BI, Dataflows Gen2, and Data Pipelines. They ensure data is properly ingested, modeled, and visualized, enabling data-driven decisions. They also monitor data health, optimize performance, and enforce governance standards.
| Feature | Benefit |
|---|---|
| Unified Storage | Single logical data lake simplifies access |
| Open Format | Uses Delta/Parquet for interoperability |
| Shortcut Support | Reuse data without duplication |
| Security | Centralized data governance policies |
| Integration |
Compatible with Lakehouse, Warehouse, and other Fabric services |
Data lineage in Fabric helps track data origin, transformation, and movement. It’s implemented using built-in capabilities within Power BI and Data Pipelines. When a dataset is created, Fabric automatically logs lineage across different components. You can visualize it in the workspace or lineage view to identify data sources, transformation steps, and dependencies, which aids in governance and troubleshooting.
| Feature | Lakehouse | Warehouse |
|---|---|---|
| Storage Format | Delta/Parquet | Proprietary SQL tables |
| Query Interface | Spark/SQL | T-SQL |
| Data Access | Both structured and unstructured | Structured only |
| Flexibility | High | Moderate |
| Use Case | Data engineering, ML | Business intelligence, reporting |
To create a Power BI report from Lakehouse data, first ensure your data is structured in Delta format within OneLake. In Power BI Desktop or Power BI Service in Fabric, connect to your Lakehouse and select the appropriate tables. Build relationships in the model view, add measures and calculated columns, and then use visualizations to represent your insights. Publish the report back to Fabric for collaboration and sharing.
Microsoft Fabric enables real-time analytics through integration with Event Streams and Data Activator. Event Streams capture and process data from IoT or API sources in real time, while Data Activator allows you to define event-based rules that trigger alerts or workflows. Coupled with streaming datasets in Power BI, Fabric supports live dashboards and real-time business decisions.
| Feature | SQL Analytics Endpoint | Spark |
|---|---|---|
| Language | T-SQL | Python, Scala, R, SQL |
| Use Case | Ad hoc querying, reporting | Data engineering, ML |
| Performance | Optimized for BI workloads | Parallel distributed computing |
| Integration | Power BI, Dataflows | Notebooks, Pipelines |
| Accessibility | Business analysts | Data engineers/scientists |
Security is implemented through a combination of workspace roles, item-level permissions, and data-level security like row-level security (RLS). You can manage access at the folder, table, or file level. Fabric also supports integration with Microsoft Purview for data governance and sensitivity labeling. Role-based access controls (RBAC) are enforced consistently across components.
Ingest real-time events from IoT, APIs, and Kafka Perform transformations before storage Route events to Lakehouse, Warehouse, or Power BI Define conditions to trigger alerts or actions Ensure real-time operational dashboards
Use techniques like table partitioning, clustering, and caching. Leverage Spark SQL for complex queries and T-SQL endpoints for fast lookups. Avoid excessive joins, filter early in the pipeline, and use optimized formats like Delta. Monitor performance using built-in Fabric monitoring tools and adjust resources as needed.
| Attribute | Dataset | Semantic Model |
|---|---|---|
| Scope | Power BI-specific | Fabric-wide |
| Use | Basic reporting | Reusable data modeling |
| Language | DAX & M | DAX |
| Storage | In Power BI workspace | OneLake-backed |
| Advanced Features | Limited | Supports calculation groups, perspectives |
Data pipelines in Fabric can be scheduled using trigger-based scheduling. You can set frequency, start/end times, and retry policies. Monitoring is available through the pipeline run history, logs, and error messages. Integration with alerts and notifications is possible using Data Activator or Azure Monitor for more complex workflows.
| Skill/Tool | Importance |
|---|---|
| Power BI | Data visualization and modeling |
| T-SQL | Structured queries and transformations |
| Spark/PySpark | Big data and parallel processing |
| DAX | Advanced measures and KPIs |
| Azure Data Factory | ETL and orchestration |
| OneLake/Delta | Unified storage and querying |
Microsoft Fabric brings data engineers, analysts, and business users into a shared platform. Using unified workspaces, version control, and shared lineage, teams can collaborate on data pipelines, reports, and models. Power BI integration allows feedback and iteration on reports, while centralized governance ensures consistent standards across the board.