Top 50 Azure Databricks Interview Questions & Answers

1. State the key components of Azure Databricks

Workspace to organize notebooks, libraries, and experiments. Notebooks offer interactive coding environments.

Clusters are virtual machines for executing Spark jobs and Jobs are automated workloads.

Data Sources are connectors for various data sources like Azure Data Lake, SQL Database, and Cosmos DB.

2. How does Databricks handle data storage and about Auto-scaling facilities?

To handle data storage, Azure Databricks does not store data and can integrate with Azure Data Lake, Blob Storage, SQL Database, Delta Lake, and other cloud storage solutions.

Auto-scaling allows clusters to scale up/down based on workload demands dynamically. It reduces costs and improves performance.

3. Explain the difference between Azure Databricks and Apache Spark?

Azure Databricks is a managed Spark service whereas Apache Spark is an open-source distributed computing framework. Azure Databricks provides built-in security, auto-scaling, and easy integration with Azure services but Apache Spark requires manual setup and management.

4. Specify the types of clusters in Azure Databricks?

Interactive Clusters are used for development and debugging.
Job Clusters can be created for running a specific job and terminated afterward.

5. What is a Driver, Worker node in Azure Databricks? Explain about Databricks Unit and Delta Lake?

Driver Node manages the Spark application, coordinates tasks, and maintains cluster state. Worker Nodes execute tasks in parallel.

DBU is a consumption-based billing unit that can be used to measure processing usage.

Delta Lake is a storage layer with ACID transactions, schema enforcement, and time travel features.

6. How can you improve Spark job performance in Azure Databricks?

Use Delta Lake for optimized data storage.
Enable Auto-Scaling and optimize Spark configurations to adjust cluster resources dynamically.
Use Broadcast Joins for small dataset joins.

7. What are the advantages of Delta Lake?

ACID Transactions ensure data consistency.
Schema Evolution allows automatic schema updates.
Time Travel enables querying previous versions of data.
Data Compaction (OPTIMIZE) improves read performance.

8. How do you optimize cluster performance in Azure Databricks?

Use auto-scaling to adjust cluster size dynamically.
For faster query execution, optimize caching and broadcast joins.
To improve storage performance, enable Delta Cache and use Photon runtime.

9. How do you create a Delta Table in Azure Databricks?

CREATE TABLE sales_data 
USING DELTA 
LOCATION 'dbfs:/mnt/datalake/sales_data';

10. How do you perform upserts in Delta Lake?

MERGE INTO target_table t 
USING source_table s 
ON t.id = s.id 
WHEN MATCHED THEN UPDATE SET t.name = s.name 
WHEN NOT MATCHED THEN INSERT (id, name) VALUES (s.id, s.name);

11. How is security managed in Azure Databricks?

Security is managed in Azure Databricks using:

Azure AD authentication
Role-Based Access Control (RBAC)
Data encryption

12. How does Azure Databricks support CI/CD in a DevOps environment?

Azure Databricks integrates with Azure DevOps for CI/CD via:

Databricks Repos for version control with GitHub/Azure Repos.
Databricks CLI and REST API for automated deployments.
Jobs API for scheduling workflows programmatically.

13. How do you enable Auto-Optimize in Delta Lake?

ALTER TABLE my_table SET TBLPROPERTIES ('delta.autoOptimize.optimizeWrite' = true);

14. How does Unity Catalog enhance data governance in Azure Databricks?

Unity Catalog is a centralized data governance solution which enhances data governance with features like:

Fine-grained access control using SQL-based permissions.
Auditing & Monitoring for compliance tracking.
Cross-workspace data sharing without duplication.

15. How does Azure Databricks integrate with Azure Data Lake?

To integrate with Azure Data Lake, Azure Databricks uses Azure Data Lake Storage Gen2. It mounts points to connect and access files stored in the lake.

16. How do you create a mount point in Databricks for Azure Data Lake?

dbutils.fs.mount(
    source="wasbs://container@storageaccount.blob.core.windows.net",
    mount_point="/mnt/datalake",
    extra_configs={"fs.azure.account.key.storageaccount.blob.core.windows.net": "your-access-key"}
)

17. How do you troubleshoot slow-running queries in Azure Databricks?

To analyze job execution, DAG, and stages, use Spark UI and check for skewed data using df.describe() and repartition if necessary.
For optimized query execution, enable Photon Engine on Databricks SQL and use Adaptive Query Execution (AQE) to optimize query plans dynamically.

18. How do you secure sensitive data in Azure Databricks?

Use Azure Key Vault for secrets management and Unity Catalog for access control.

19. What is Structured Streaming in Databricks?

Structured Streaming is a scalable and fault-tolerant real-time streaming framework built on Spark which supports:

Incremental data processing and Fault tolerance.
Scalability by handling high-throughput data streams.
Integration with Delta Lake, Kafka, Event Hubs, and other Azure services.

20. How do you process streaming data in Azure Databricks?

from pyspark.sql.functions import *

stream_df = spark.readStream.format("csv").option("header", "true").load("/mnt/datalake/stream/")

stream_df.writeStream.format("delta").outputMode("append").option("checkpointLocation", "/mnt/checkpoints/").start()

21. What are the different output modes in Structured Streaming?

Append – Writes only new records to the sink.
Complete – Rewrites the entire output table with each trigger.
Update – Updates only changed rows in the output.

22. How do you enable Photon in Azure Databricks?

Photon is a performance acceleration engine for SQL workloads. Enable it by selecting “Enable Photon” in the cluster settings.

23. What are Databricks Widgets, and how are they used?

Widgets allow passing dynamic parameters to notebooks. Example usage:

dbutils.widgets.text("param1", "default_value")
param_value = dbutils.widgets.get("param1")
print(param_value)

24. What is MLflow in Azure Databricks?

MLflow is an open-source machine learning lifecycle tool integrated into Databricks for tracking, managing, and deploying ML models.

25. How does Databricks ensure fault tolerance in Structured Streaming?

Checkpointing – Stores progress metadata in Azure Data Lake / DBFS to recover from failures.
Write-Ahead Logs (WAL) – Ensures exactly-once processing.
Idempotent Writes – Using Delta Lake’s ACID transactions to prevent duplicate writes.

26. An online shopping website wants to track customer clicks in real-time to analyze user behavior. State the approach to track the customer clicks

Ingest clickstream data from Kafka.
Perform session-based aggregation (e.g., number of clicks per user).
Store results in a Databricks SQL table for reporting.

27. Explain about Exactly-Once Processing with Idempotent Writes

Databricks ensures exactly-once processing even if the job fails or restarts by:

Idempotent Writes in Delta Lake (ensuring no duplicate entries).
Transactional Writes via Spark’s Write-Ahead Logs (WAL).

28. Propose a solution to monitor system logs and detect error patterns in real-time.

Read log data from Azure Data Lake.
Use Regex pattern matching to identify errors.
Send alerts to Azure Monitor or Kafka.

29. How Multi-Hop Streaming Architecture organizes streaming data?

Bronze Layer (Raw Data)
Silver Layer (Cleaned & Transformed Data)
Gold Layer (Aggregated, Business-Ready Data)

30. How does Databricks Repos help in version control?

Databricks Repos allows users to integrate Databricks with GitHub, Azure DevOps, and Bitbucket to version-control notebooks and collaborate efficiently.

Enables collaborative development.
Supports branching, merging, and pull requests.
Simplifies CI/CD pipeline integration.

31. How does Auto Loader work in Azure Databricks?

Auto Loader is a streaming ingestion tool that efficiently loads data from cloud storage into Delta Lake.

Incremental file discovery (processes only new files).
Schema evolution support.
Scalable performance for real-time data ingestion.

32. How does Databricks handle schema evolution in Delta Lake?

Delta Lake allows schema evolution by using the mergeSchema option. It ensures that new columns are automatically added while preserving existing schema integrity.

Example:

df.write.format("delta").mode("append").option("mergeSchema", "true").save("/mnt/delta/table")

33. Explain the Databricks File System (DBFS).

DBFS is a distributed file system in Azure Databricks that allows users to store and manage files easily.

Provides mount points to Azure Blob Storage or ADLS Gen2.
Supports structured, semi-structured, and unstructured data.
Uses /dbfs/ as the default file path.

34. How does Unity Catalog enhance security?

Unity Catalog is a centralized governance tool for managing access control, metadata, and security across multiple workspaces.

Implements fine-grained access control (RBAC).
Supports column-level security.
Provides audit logs for compliance.

35. What are Databricks SQL Warehouses, and how do they differ from clusters?

Databricks SQL Warehouses are optimized for running SQL workloads.

Faster performance with Photon Engine.
Designed for BI and dashboarding use cases.
Supports serverless execution (no cluster management).

36. What are Databricks Runtime versions, and why are they important?

Databricks Runtime is a pre-configured environment that includes Apache Spark, MLflow, TensorFlow, and other libraries for specific workloads.

Databricks Runtime for Machine Learning: Includes MLflow, TensorFlow, and other ML libraries.
Databricks Runtime for Genomics: Optimized for processing large-scale genomic data.
Databricks Runtime with Photon: Provides faster query execution using a vectorized engine.

37. What is the difference between Auto Scaling and Auto Termination in Databricks?

Auto Scaling: Dynamically adjusts cluster size based on workload demand to optimize cost.
Auto Termination: Automatically shuts down idle clusters to prevent unnecessary charges.

38. How can you optimize the performance of a Databricks notebook?

To optimize the performance of a Databricks notebook:

Use Delta Lake and optimize queries with Z-Ordering and Data Skipping.
Broadcast joins for small tables using broadcast(df).
Enable Photon Engine for faster query execution.

39. What is the role of Databricks Runtime?

Databricks Runtime is a customized execution environment for running Apache Spark workloads on Azure Databricks.

Optimized Spark execution for better performance.
Photon Engine for accelerated query processing.
Built-in Machine Learning libraries (MLflow, AutoML).
Delta Engine for optimized performance on Delta Lake.

40. How does the databricks autoloader simplify data ingestion?

Databricks Autoloader is a streaming ingestion tool for handling large-scale data. It automatically detects new files in cloud storage, supports schema evolution and incremental ingestion. Databricks autoloader handles complex file formats like JSON, CSV, Avro, and Parquet.

41. How do you enable incremental processing in Databricks?

Databricks enables incremental processing using Delta Lake’s Change Data Feed (CDF) or Structured Streaming.

Example:

df = spark.readStream.format("delta") \
    .option("readChangeFeed", "true") \
    .table("delta_table")

42. How does Databricks integrate with Apache Kafka for real-time processing?

Databricks reads and writes streaming data to Apache Kafka using Spark Structured Streaming.

Example:

Reading from Kafka:

df = spark.readStream.format("kafka") \
    .option("kafka.bootstrap.servers", "server:9092") \
    .option("subscribe", "transactions") \
    .load()

43. What is Databricks SQL, and how does it differ from Spark SQL?

Databricks SQL is a serverless data warehouse solution optimized for BI workloads. Databricks SQL Use Case run ad-hoc queries on Delta Lake tables.

Spark SQL is an engine for running distributed queries within Apache Spark. Spark SQL Use Case processes large-scale structured and semi-structured data.

44. What are the benefits of Databricks Workflows?

Databricks Workflows is a native orchestration tool used for automating and scheduling ETL pipelines.

Supports multi-task workflows (dependent job execution).
Built-in retry mechanisms for failure recovery.
Integrates with Azure Data Factory and Airflow for advanced scheduling.

45. How does delta sharing enable secure data collaboration?

Delta Sharing is an open protocol that allows secure sharing of live data across different platforms without data replication.

Cross-cloud sharing (Databricks, Snowflake, AWS, GCP).
Supports real-time data updates.
Read-only access ensures data security.

46. How do you secure sensitive data in Databricks?

Databricks supports data encryption and column masking for securing Personally Identifiable Information.

Managed Keys Encryption using Azure Key Vault.
Dynamic Data Masking with VIEWs.

47. What is the role of Time Travel in Delta Lake?

Databricks Time Travel allows users to query older versions of data in Delta Lake.

Rollback to previous data states.
Audit changes over time.

48. How does Databricks optimize Delta Lake tables?

OPTIMIZE: Merges small files into larger ones to reduce read latency.
VACUUM: Deletes older versions of data to free up storage.

Example:

OPTIMIZE sales_data ZORDER BY (customer_id);
VACUUM sales_data RETAIN 7 HOURS;

49. How do you implement Cross-Region Disaster Recovery for Azure Databricks?

Databricks enables Disaster Recovery (DR) using:

Cross-region replication of Delta tables using ADLS Gen2 replication.
Automated job failover by maintaining a secondary Databricks workspace.
Cluster state backups using Terraform or Databricks CL.

50. How does Databricks implement real-time anomaly detection?

Databricks integrates Structured Streaming to detect anomalies in real time with Machine Learning models.

Read streaming data from Kafka, ADLS, or Event Hubs.
Use a pre-trained ML model to detect anomalies.
Write flagged anomalies to a Delta Lake table or alert system.

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Top 50 Azure Databricks Interview Questions & Answers