Azure Data Factory Interview Questions

Azure Data Factory (ADF) is a powerful cloud-based data integration service from Microsoft that allows users to create, schedule, and orchestrate data pipelines. Whether you’re a beginner or an experienced professional, preparing for an Azure Data Factory interview requires knowledge of its core concepts, features, and advanced functionalities. Here’s a comprehensive list of Top 50 Azure Data Factory Interview Questions to help you ace your interview.

Basic Azure Data Factory Interview Questions

1. What is Azure Data Factory?

Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines to move and transform data across various sources and destinations.

2. What are the core components of Azure Data Factory?

• Pipelines

• Activities

• Datasets

• Linked Services

• Triggers

• Integration Runtimes

3. What is a Pipeline in Azure Data Factory?

A pipeline is a group of activities that perform data transformation and movement tasks.

4. What is a Linked Service in Azure Data Factory?

A Linked Service is a connection string that defines the connection information needed for Azure Data Factory to connect to external data sources.

5. What is a Dataset in Azure Data Factory?

A Dataset represents the data structure within the data store. It defines the schema and location of data to be used in activities.

6. What is Integration Runtime in Azure Data Factory?

Integration Runtime is the compute infrastructure used to perform data movement and data transformation activities.

7. What are Triggers in Azure Data Factory?

Triggers are used to schedule pipeline executions based on time or events.

8. What types of Integration Runtimes are available in Azure Data Factory?

• Azure Integration Runtime

• Self-hosted Integration Runtime

• Azure-SSIS Integration Runtime

9. How does Azure Data Factory differ from SSIS?

Azure Data Factory is a cloud-based service, while SSIS is an on-premises ETL tool. ADF supports hybrid data movement and has better scalability and monitoring features.

10. What are the different types of triggers in Azure Data Factory?

• Schedule Trigger

• Tumbling Window Trigger

• Event-Based Trigger

Intermediate Azure Data Factory Interview Questions

11. How do you monitor pipeline executions in Azure Data Factory?

Azure Data Factory provides built-in monitoring features where users can view pipeline execution history, activity runs, and trigger runs through the Azure portal.

12. What is the difference between Tumbling Window and Schedule Trigger?

Tumbling Window Trigger processes data in fixed-sized, non-overlapping time windows, while Schedule Trigger runs pipelines at regular intervals.

13. How can you handle errors in Azure Data Factory pipelines?

You can implement retry policies, logging, and alerts to handle errors in Azure Data Factory pipelines.

14. What is a Lookup Activity in Azure Data Factory?

Lookup Activity is used to retrieve data from an external source to be used in subsequent activities.

15. What is the difference between Copy Activity and Data Flow Activity?

Copy Activity moves data from source to destination, while Data Flow Activity performs data transformations.

16. How can you secure sensitive information in Azure Data Factory?

Sensitive information can be secured using Azure Key Vault and secure string parameters.

17. What are Parameters in Azure Data Factory?

Parameters are user-defined values that can be passed to pipelines, datasets, and linked services at runtime.

18. What is the purpose of ForEach Activity?

ForEach Activity iterates over a collection and executes child activities for each item in the collection.

19. How can you debug pipelines in Azure Data Factory?

You can use the Debug option in the Azure portal to run pipelines in test mode and view activity logs.

20. What is the difference between Pipeline Parameters and Dataset Parameters?

Pipeline Parameters are passed at pipeline execution time, while Dataset Parameters are used to define dataset properties dynamically.

Advanced Azure Data Factory Interview Questions

21. How can you optimize data pipelines in Azure Data Factory?

• Use parallel processing

• Optimize data partitioning

• Minimize data movement

• Use appropriate Integration Runtime

22. What is Data Flow Debugging?

Data Flow Debugging allows you to interactively test and debug your data flows before publishing.

23. How can you implement CI/CD pipelines with Azure Data Factory?

CI/CD pipelines can be implemented using Azure DevOps and ARM templates.

24. What is Managed Virtual Network in Azure Data Factory?

Managed Virtual Network provides enhanced security by enabling secure communication between Azure Data Factory and data sources without exposing data to the public internet.

25. How do you perform Incremental Data Loads in Azure Data Factory?

Incremental data loads can be performed using watermark columns, Lookup Activities, and stored procedures.

26. What is Data Flow in Azure Data Factory?

Data Flow is a visual, code-free data transformation feature that enables ETL processing within Azure Data Factory.

27. How do you implement Error Row Handling in Data Flows?

Error Row Handling can be implemented using error handling transformations like Assert and Alter Row.

28. What is the difference between Azure Blob Storage and Azure Data Lake Storage in Azure Data Factory?

Azure Blob Storage is optimized for unstructured data, while Azure Data Lake Storage is optimized for big data analytics with hierarchical namespace support.

29. How can you schedule pipeline execution based on file arrival?

Event-Based Triggers can be used to schedule pipelines based on file arrival in storage accounts.

30. What is the purpose of the Get Metadata Activity?

Get Metadata Activity retrieves metadata information about files and folders in storage.

Scenario-Based Azure Data Factory Interview Questions

31. How would you design a pipeline to copy data from SQL Server to Azure Blob Storage?

Define Linked Services, Datasets, and a Copy Activity with appropriate mappings.

32. How can you implement a data archival process in Azure Data Factory?

Use pipelines with Copy Activity to move old data to archive storage based on certain conditions.

33. How do you implement pipeline dependency in Azure Data Factory?

Pipeline dependencies can be implemented using Trigger Dependencies and Wait Activities.

34. How would you handle duplicate data during data ingestion?

Use Data Flow transformations like Aggregate and Filter to remove duplicate records.

35. How can you log pipeline execution details?

Use Web Activities to log execution details to external logging services like Azure Log Analytics.

Best Practices Azure Data Factory Interview Questions

36. How can you improve pipeline performance?

• Use partitioning

• Minimize data movement

• Optimize query performance

37. How do you secure data in transit?

Enable HTTPS and use Azure Private Link for secure data movement.

38. What is the purpose of Pipeline Concurrency?

Pipeline Concurrency controls the number of parallel pipeline runs allowed.

39. How do you implement Retry Policies?

Retry Policies can be configured at activity level to automatically retry failed executions.

40. How can you version control Azure Data Factory artifacts?

Use Azure DevOps Git integration for version controlling pipelines, datasets, and linked services.