Top 25 Interview Questions and Answers for Azure Data Fabric

Azure Data Fabric (often aligned with Microsoft Fabric concepts) is becoming a must-know skill for data engineers, analytics professionals, and cloud architects. Interviewers now expect candidates to understand end-to-end data integration, analytics, governance, and AI workloads within a unified Azure ecosystem.

This blog covers the Top 25 Azure Data Fabric interview questions with detailed answers, ranging from basics to advanced scenarios.

1. What is Azure Data Fabric?

Azure Data Fabric is a unified analytics platform that brings together data ingestion, storage, engineering, science, real-time analytics, and business intelligence into a single SaaS-based experience on Azure. It eliminates data silos by providing a single source of truth using OneLake.

2. How is Azure Data Fabric different from traditional data platforms?

Traditional platforms use separate tools for ingestion, storage, analytics, and reporting. Azure Data Fabric:

• Uses OneLake as a unified storage layer
• Provides integrated services (Data Engineering, Data Factory, Synapse, Power BI)
• Reduces data movement
• Offers built-in governance and security

3. What is OneLake in Azure Data Fabric?

OneLake is a single, centralized data lake for the entire organization.
It:

• Stores structured and unstructured data
• Eliminates multiple data copies
• Supports open formats like Delta Parquet
• Works seamlessly with Power BI, Spark, and SQL

4. What are the key components of Azure Data Fabric?

Azure Data Fabric includes:

• Data Factory – Data ingestion and pipelines
• Data Engineering – Spark-based transformations
• Data Science – ML and AI workloads
• Data Warehouse – SQL analytics
• Real-Time Analytics – Streaming data
• Power BI – Reporting and visualization

5. What is the role of Azure Data Factory within Data Fabric?

Azure Data Factory handles:

• Data ingestion from on-prem and cloud sources
• ETL and ELT pipelines
• Scheduling and monitoring
• Data transformation orchestration

It acts as the data movement backbone.

6. What data formats are supported in Azure Data Fabric?

Azure Data Fabric supports:

• Parquet
• Delta Lake
• CSV
• JSON
• Avro

Delta format is preferred for ACID transactions and performance.

7. What is Delta Lake and why is it important?

Delta Lake adds:

• ACID transactions
• Schema enforcement
• Time travel
• Versioning

This ensures reliable analytics on large datasets.

8. How does Azure Data Fabric support real-time analytics?

It supports:

• Streaming ingestion
• Event-based processing
• Near real-time dashboards
• Integration with IoT and event streams

This is useful for fraud detection, monitoring, and alerts.

9. What is the difference between Data Engineering and Data Science in Fabric?

10. How does Power BI integrate with Azure Data Fabric?

Power BI:

• Connects directly to OneLake
• Supports Direct Lake mode
• Provides real-time dashboards
• Eliminates data duplication

This improves report performance and freshness.

11. What is Direct Lake mode?

Direct Lake mode allows Power BI to:

• Query data directly from OneLake
• Avoid import or DirectQuery limitations
• Deliver faster performance

12. How is security handled in Azure Data Fabric?

Security features include:

• Azure Active Directory integration
• Role-based access control (RBAC)
• Data-level security
• Encryption at rest and in transit

13. What is data governance in Azure Data Fabric?

Data governance ensures:

• Data quality
• Lineage tracking
• Metadata management
• Compliance and auditing

It improves trust and regulatory compliance.

14. What is data lineage and why is it important?

Data lineage shows:

• Where data originated
• How it was transformed
• Where it is consumed

It helps with impact analysis and debugging.

15. How does Azure Data Fabric support AI workloads?

It supports:

• Built-in notebooks
• Integration with Azure ML
• Model training and deployment
• AI-driven insights

16. What are notebooks in Azure Data Fabric?

Notebooks are interactive environments used for:

• Data exploration
• Data transformation
• Machine learning
• Visualization

They support Python, SQL, and Spark.

17. What is ELT and how is it used in Data Fabric?

ELT stands for:

• Extract
• Load
• Transform

In Data Fabric, data is loaded first into OneLake and transformed later using Spark or SQL for better scalability.

18. How does Azure Data Fabric improve performance?

Performance is improved through:

• Unified storage
• Reduced data movement
• In-memory analytics
• Optimized query engines

19. What is the difference between Azure Synapse and Azure Data Fabric?

Azure Data Fabric:

• Is SaaS-based
• Provides unified analytics
• Simplifies management

Azure Synapse:

• Requires manual integration
• Focuses mainly on data warehousing

20. What are common use cases of Azure Data Fabric?

Common use cases include:

• Enterprise analytics
• Customer 360 dashboards
• IoT analytics
• Financial reporting
• AI-driven insights

21. How does Azure Data Fabric support scalability?

It supports:

• Auto-scaling compute
• Distributed processing
• Cloud-native elasticity

22. What is the role of metadata in Data Fabric?

Metadata:

• Describes data structure
• Enables search and discovery
• Supports governance and lineage

23. What challenges does Azure Data Fabric solve?

It solves:

• Data silos
• Tool sprawl
• High maintenance costs
• Slow analytics workflows

24. Who should learn Azure Data Fabric?

Ideal for:

• Data engineers
• BI professionals
• Cloud architects
• Analytics beginners
• IT professionals transitioning to data roles

25. Why is Azure Data Fabric important for future careers?

Azure Data Fabric aligns with:

• AI-driven analytics
• Cloud-first strategies
• Unified data platforms

It is a high-demand skill with strong career growth.

Conclusion

Azure Data Fabric is reshaping how organizations manage and analyze data. Preparing these top 25 interview questions will help you confidently crack interviews for Data Engineer, Analytics Engineer, and Azure Cloud roles.