Top 25 Interview Questions and Answers for Azure Databricks

Azure Databricks is one of the most in-demand data engineering and analytics platforms, combining Apache Spark with Microsoft Azure’s cloud power. Whether you’re preparing for a data engineer, data analyst, or big data developer role, mastering Azure Databricks interview questions is essential.

This blog covers the top 25 Azure Databricks interview questions with clear, accurate answers, suitable for beginners and professionals alike.

1. What is Azure Databricks?

Azure Databricks is an Apache Spark–based analytics platform optimized for Microsoft Azure. It is designed for big data processing, machine learning, and real-time analytics. Databricks provides a collaborative workspace, auto-scaling clusters, and deep integration with Azure services like Data Lake, Synapse, and Power BI.

2. What are the key components of Azure Databricks?

Azure Databricks consists of:

• Workspace – Collaborative notebooks and dashboards
• Clusters – Compute resources for running Spark jobs
• Notebooks – Support for Python, SQL, Scala, and R
• Jobs – Scheduled or automated workloads
• Databricks File System (DBFS) – Distributed file storage

3. What is Apache Spark and how does Databricks use it?

Apache Spark is an open-source distributed data processing engine known for in-memory computation and high performance. Azure Databricks is built on Spark and enhances it with:

• Optimized runtime
• Auto-scaling clusters
• Better security and monitoring
• Simplified Spark management

4. What is a Databricks cluster?

A Databricks cluster is a set of virtual machines used to run Spark workloads. It includes:

• Driver node – Controls job execution
• Worker nodes – Perform data processing

Clusters can be interactive or job-based, and they can auto-scale based on workload.

5. Difference between Interactive Cluster and Job Cluster?

6. What languages are supported in Azure Databricks?

Azure Databricks supports:

• Python (PySpark)
• SQL
• Scala
• R

Multiple languages can be used within the same notebook.

7. What is a Databricks Notebook?

A Databricks Notebook is a web-based interface for writing and executing code. It supports data visualization, markdown documentation, and collaborative editing.

8. What is DBFS (Databricks File System)?

DBFS is a distributed file system that allows Databricks to access Azure Blob Storage and Azure Data Lake as if they were local file systems. It uses the dbfs:/ path.

9. How does Azure Databricks integrate with Azure Data Lake Storage?

Azure Databricks integrates with ADLS using:

• OAuth or Service Principal authentication
• Mount points or direct access
• High-performance Spark connectors

This allows seamless big data processing on large datasets.

10. What is Delta Lake?

Delta Lake is a storage layer built on top of data lakes that provides:

• ACID transactions
• Schema enforcement
• Time travel (data versioning)
• Reliable streaming and batch processing

11. What is the advantage of Delta Lake over Parquet?

Delta Lake improves Parquet by adding:

• Data consistency
• Rollback to previous versions
• Support for upserts and deletes
• Better performance with indexing

12. What is Spark SQL?

Spark SQL is a Spark module that allows querying structured data using SQL syntax. In Databricks, it enables:

• Querying Delta tables
• Integration with BI tools
• Faster analytics on big data

13. What is Auto Scaling in Azure Databricks?

Auto Scaling automatically adds or removes worker nodes based on workload demand. This helps:

• Optimize performance
• Reduce costs
• Handle sudden data spikes

14. What is Auto Termination?

Auto Termination shuts down idle clusters after a defined time, preventing unnecessary compute costs.

15. How is security handled in Azure Databricks?

Azure Databricks uses:

• Azure Active Directory (AAD) authentication
• Role-based access control (RBAC)
• Secure secrets using Databricks Secret Scope
• Network isolation with VNET injection

16. What is a Databricks Job?

A Databricks Job is a scheduled or triggered task that runs notebooks, JARs, or Python scripts automatically for production workloads.

17. What is MLflow in Azure Databricks?

MLflow is an open-source machine learning lifecycle tool used for:

• Experiment tracking
• Model versioning
• Model deployment

Azure Databricks has built-in MLflow integration.

18. What is a Spark DataFrame?

A Spark DataFrame is a distributed collection of data organized into named columns. It is similar to a table in a database and supports SQL-like operations.

19. Difference between RDD and DataFrame?

20. What is caching in Databricks?

Caching stores frequently accessed data in memory, reducing computation time and improving query performance.

21. How does Databricks handle streaming data?

Databricks uses Spark Structured Streaming to process real-time data from sources like:

• Event Hubs
• Kafka
• Azure IoT Hub

22. What is a mount point in Databricks?

A mount point connects external storage (like ADLS) to DBFS, allowing users to access data using simple file paths.

23. What is Photon in Azure Databricks?

Photon is a high-performance query engine that accelerates SQL and Delta Lake workloads using vectorized processing.

24. How does Azure Databricks differ from Azure Synapse?

• Databricks: Best for big data engineering and machine learning
• Synapse: Best for enterprise data warehousing and SQL analytics

They are often used together.

25. Why should companies use Azure Databricks?

Companies use Azure Databricks because it offers:

• Faster data processing
• Unified analytics and ML
• Cost-efficient scaling
• Strong Azure ecosystem integration

Conclusion

Azure Databricks is a must-have skill for modern data professionals. These top 25 interview questions and answers will help you confidently tackle interviews for data engineering, analytics, and cloud roles.