Top 25 Interview Questions With Answers For DP-600T00

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.

Top 25 Interview Questions with Answers for DP-600T00: Microsoft Fabric Analytics Engineer

1. What is Microsoft Fabric and how does it relate to Power BI?

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.

2. Explain the concept of a Lakehouse in Microsoft Fabric.

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.

3. What is the role of a Microsoft Fabric Analytics Engineer in an organization?

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.

4. List the major components of Microsoft Fabric.

• Power BI: For visualization and reporting
• Data Factory: For ETL/ELT pipelines
• Synapse Data Engineering: For big data and Spark-based analytics
• Synapse Data Warehousing: T-SQL-based structured querying
• OneLake: Unified data lake storage
• Data Activator: For event-driven data alerts
• Lakehouse and Warehouse objects: For storing and querying data

5. What are Dataflows Gen2 and how are they different from Gen1?

• Built natively into Microsoft Fabric
• Directly integrated with OneLake
• Supports parallel processing and improved performance
• Better data lineage and observability
• Supports connections to multiple destinations within Fabric

6. What are the benefits of using OneLake in Microsoft Fabric?

7. How do you implement data lineage in Microsoft Fabric?

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.

8. What are best practices for building Lakehouse models in Fabric?

• Use Delta tables for reliable data storage
• Partition large tables for performance
• Maintain a layered approach: Raw → Bronze → Silver → Gold
• Use notebooks for data cleaning and enrichment
• Avoid hard coding paths; use dynamic parameters and shortcuts

9. Explain how you can use Notebooks in Microsoft Fabric.

• Use Python, Spark, or SQL within a single environment
• Perform advanced analytics on top of Lakehouse data
• Schedule notebooks with pipelines for automation
• Integrate with ML models or visualizations
• Collaborate and share notebook content securely

10. What are the differences between Lakehouse and Warehouse in Microsoft Fabric?

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.

12. What types of pipelines can you create in Fabric’s Data Factory?

• Ingestion Pipelines: Move data from on-prem/cloud to OneLake
• Transformation Pipelines: Apply data cleaning or enrichment
• Loading Pipelines: Push data into Lakehouse or Warehouse
• Notebook-Driven Pipelines: Run Spark/SQL transformations
• Scheduled Pipelines: Automate recurring jobs

13. What is the role of Shortcuts in OneLake?

• Point to external or internal data locations without copying
• Allow data reuse across workspaces
• Improve storage efficiency
• Enhance collaboration across departments
• Simplify data cataloging and lineage

14. How does Microsoft Fabric support real-time analytics?

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.

15. What are the differences between SQL Analytics Endpoint and Spark in Fabric?

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.

17. What is the purpose of the Semantic Model in Microsoft Fabric?

• Acts as a data model layer for Power BI and other tools
• Defines relationships, hierarchies, and KPIs
• Allows DAX calculations
• Optimizes data for visualization
• Can be reused across reports and dashboards

18. Explain the use of Event Streams in Fabric.

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

19. How can you optimize performance of queries in Lakehouse?

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.

20. What’s the difference between Datasets and Semantic Models in Fabric?

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.

22. What are some common challenges when working with Fabric Lakehouse?

• Managing metadata consistency
• Handling schema evolution in Delta tables
• Optimizing performance for large queries
• Security and access control at granular levels
• Integration with external tools like Azure ML

23. How do you integrate Microsoft Fabric with external tools or platforms?

• Use REST APIs for automation
• Leverage Power BI Embedded for third-party apps
• Connect with Azure Synapse, Azure ML, or external notebooks
• Use Fabric shortcuts to reference external data sources
• Export datasets to external data lakes or databases

24. What skills or tools are essential for a Fabric Analytics Engineer?

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.