Azure Databricks Interview Questions – A Practical Guide for Data Engineers

Real-world, scenario-driven questions asked in mid to senior Data Engineering interviews

Azure Databricks has become a core platform for building modern Lakehouse architectures on Azure. Interviewers today focus less on definitions and more on real-world implementation, performance, governance, and cost optimization.

This blog presents a curated and logically sequenced list of Azure Databricks interview questions, covering ingestion, transformation, governance, CI/CD, streaming, security, and architecture — exactly how interviews flow in real scenarios.

Section 1: Databricks & Lakehouse Fundamentals

1. Why Databricks? Explain the architecture of Azure Databricks

Core components (Control Plane, Data Plane)
Why Databricks over traditional Spark or Synapse
Role in Lakehouse architecture

2. What is Lakehouse Medallion Architecture (Bronze / Silver / Gold)?

Benefits and demerits
How it compares with traditional data warehouses
When it may not be the right choice

3. What are the benefits of Delta Lake file format?

ACID transactions
Schema enforcement & evolution
Time Travel
Performance optimizations

4. External vs Internal (Managed) Tables in Databricks

Storage ownership
Use cases
Governance implications with Unity Catalog

Section 2: Data Ingestion & Incremental Processing

5. What is Databricks Auto Loader and how does it perform incremental loading?

File notification vs directory listing
Exactly-once semantics
Schema inference & evolution

6. How do you implement CDC (Change Data Capture) in Databricks?

CDC from source systems
Merge-based CDC
Streaming vs batch CDC
Delta Change Data Feed (CDF)

7. Explain SCD Type 1 vs SCD Type 2. When do you use each?

Business scenarios
Storage vs history trade-offs

8. Write and explain an SCD Type 2 MERGE command

Surrogate keys
Active flags
Effective start/end dates

Section 3: Delta Lake Internals & Performance

9. What is Delta Time Travel? How is it implemented?

Version-based vs timestamp-based queries
Use cases (debugging, rollback)

10. What are OPTIMIZE and VACUUM commands? When do you use them?

Small file problem
Retention periods
Cost vs performance trade-offs

11. What is Liquid Clustering? How is it better than Z-ORDER?

Dynamic clustering
No fixed columns
When Z-ORDER still makes sense

12. How do you scale a Databricks pipeline?

Horizontal vs vertical scaling
Auto-scaling clusters
Partitioning strategies
Streaming scale considerations

Section 4: Streaming & Real-Time Processing

13. Explain Databricks Streaming concepts

Watermarking
Window functions
Tumbling vs sliding windows

14. What is foreachBatch? When do you use it?

Stateful streaming
Streaming + MERGE patterns
Idempotent writes

Section 5: Data Quality & Modeling

15. How do you implement data quality checks in Medallion Architecture?

Bronze: schema & null checks
Silver: business validations
Gold: aggregations & reconciliation
Tools (DLT expectations, custom checks)

16. Dimensional Modeling: Star vs Snowflake Schema

Benefits and trade-offs
Query performance
Maintenance complexity
When to choose which

Section 6: Security, Governance & Compliance

17. How does Unity Catalog enable data governance in Databricks?

Centralized metastore
Fine-grained access control
Audit logging

18. Explain catalogs, schemas, and metastores in Unity Catalog

Hierarchy
Privileges and access patterns

19. How do you implement column-level and row-level security (PII masking)?

Dynamic views
Column masking policies
Role-based filtering

20. How do you secure Databricks resources and clusters?

Cluster policies
Network isolation
Secrets & credential passthrough

Section 7: Cloud Integration & Migration

21. How do you connect Databricks with ADLS Gen2 securely?

ABFS connector
External credentials
Managed identities

22. On-prem to Azure cloud migration – explain the end-to-end steps

Assessment
Data transfer
Schema conversion
Validation & optimization

23. Explain Open Delta Sharing

Secure data sharing outside Databricks
Cross-org / cross-cloud use cases

Section 8: Cost Optimization & Monitoring

24. How do you optimize costs on Azure Databricks?

Cluster sizing
Job vs all-purpose clusters
Spot instances
Storage optimizations

25. How do you compute Databricks costs without using Azure Portal?

System tables
Information schema
Usage & billing views

Section 9: Orchestration, CI/CD & DevOps

26. How do you schedule Databricks jobs?

Jobs UI
Cron schedules
Event-driven triggers

27. What are the types of triggers in Databricks Workflows?

Time-based
File arrival
Job completion

28. Explain CI/CD in Databricks using Azure DevOps

Git integration
Databricks Asset Bundles (DAB)
Environment promotion (Dev → QA → Prod)

Section 10: Testing & Code Quality

29. How do you implement unit testing in Databricks?

PyTest
Mocking Spark sessions
Testing transformations

30. What Python and SQL quality tools do you use?

Black – code formatting
SQLFluff – SQL linting
Importance of readable, maintainable pipelines

Section 11: Cluster Configuration & Policies

31. What cluster configurations have you used in Databricks?

Job vs interactive clusters
Auto-scaling
Photon-enabled clusters

32. How do cluster policies help with security and cost control?

Restrict node types
Enforce tagging
Prevent misuse

Section 12: Azure Ecosystem Awareness

33. What is the role of Azure Data Factory (ADF) in Databricks projects?

Orchestration vs transformation
When to use ADF vs Databricks Workflows

34. What is Azure Purview (Microsoft Purview)?

Data discovery
Lineage
Governance integration with Databricks

Final Thoughts

These questions reflect how real Databricks interviews are conducted today — focused on:

Architecture decisions
Performance tuning
Security & governance
Cost awareness
Production-ready pipelines

If you master these areas, you are well-prepared for mid to senior-level Azure Databricks roles.