Freshers / Beginner level questions & answers
Ques 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 that can move data between various supported data stores.
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Ques 2. What is a Linked Service in Azure Data Factory?
A Linked Service is a named connection to an external data store, such as Azure SQL Database, Azure Blob Storage, or on-premises SQL Server.
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Ques 3. What is the purpose of Azure Data Factory pipelines?
Pipelines define the logical flow of data and activities in Azure Data Factory. They are used to orchestrate and automate data workflows.
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Ques 4. What is a Dataset in Azure Data Factory?
A dataset represents the structure of the data that is being processed. It can be an input or output of an activity in a pipeline.
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Ques 5. Explain the concept of dynamic content in Azure Data Factory.
Dynamic content allows you to use expressions and system variables to parameterize values in your pipeline, making them more flexible and reusable.
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Ques 6. What is the purpose of Azure Data Factory triggers?
Triggers in Azure Data Factory are used to automate the execution of pipelines based on a schedule or an event.
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Ques 7. What is the purpose of the Azure Data Factory Copy Data tool?
The Copy Data tool is a wizard-based tool in Azure Data Factory that helps you create, configure, and execute copy data activities for moving data between supported sources and destinations.
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Intermediate / 1 to 5 years experienced level questions & answers
Ques 8. Explain the key components of Azure Data Factory.
Key components include datasets, linked services, pipelines, activities, and data flows.
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Ques 9. How is data movement handled in Azure Data Factory?
Data movement is achieved through activities in pipelines. Activities can be copy data activities, data flow activities, or custom activities.
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Ques 10. Explain the difference between a pipeline and a data flow in Azure Data Factory.
A pipeline defines the overall process and workflow, while a data flow defines the data transformations within that process.
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Ques 11. Explain Azure Data Factory Data Flow.
Data Flow is a cloud-based data transformation service that enables data transformations and manipulations using a visual interface.
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Ques 12. What is Azure Data Factory Integration Runtimes?
Integration Runtimes define the compute infrastructure used by data factory for data movement and data transformation activities.
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Ques 13. How can you monitor and manage Azure Data Factory?
Azure Data Factory provides monitoring dashboards, logging, and integration with Azure Monitor for tracking and managing pipeline executions.
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Ques 14. What is the difference between Azure Data Factory and Azure Logic Apps?
Azure Data Factory is primarily focused on data integration and ETL, while Azure Logic Apps are designed for workflow automation and business process integration.
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Ques 15. How can you parameterize datasets in Azure Data Factory?
Datasets can be parameterized using expressions and system variables to make them more dynamic and adaptable to changing requirements.
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Ques 16. Explain the concept of data slicing in Azure Data Factory.
Data slicing is the division of data into time-based slices, which is often used in incremental data loading scenarios in data pipelines.
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Ques 17. How does Azure Data Factory support hybrid data scenarios?
Azure Data Factory supports hybrid data scenarios through on-premises data gateways, which allow data movement between on-premises and cloud data stores.
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Ques 18. Explain the concept of Azure Data Factory Data Flow Debug Mode.
Data Flow Debug Mode allows you to interactively debug and validate data flows during development to identify and fix issues.
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Ques 19. Explain the concept of data lineage in Azure Data Factory.
Data lineage in Azure Data Factory provides a visual representation of the flow and transformation of data throughout the pipeline, helping in tracking and understanding data movements.
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Ques 20. What is the purpose of the Azure Data Factory REST API?
The REST API allows you to programmatically manage and monitor Azure Data Factory resources, such as pipelines, datasets, and activities.
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Ques 21. Explain the concept of integration patterns in Azure Data Factory.
Integration patterns in Azure Data Factory define how data is moved and transformed, providing flexibility and adaptability to different data integration scenarios.
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Ques 22. How does Azure Data Factory support data wrangling?
Azure Data Factory supports data wrangling through the Data Flow feature, which provides a visual interface for designing and executing data transformations.
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Ques 23. How can you parameterize linked services in Azure Data Factory?
Linked services can be parameterized using dynamic content expressions, allowing for dynamic configuration based on runtime values.
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Experienced / Expert level questions & answers
Ques 24. How does Azure Data Factory handle security?
Azure Data Factory supports Azure role-based access control (RBAC) to control access to resources. It also provides features like managed private endpoints for secure data movement.
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Ques 25. Explain the concept of fault tolerance in Azure Data Factory.
Azure Data Factory provides fault tolerance through retry policies for activities, and it supports monitoring and logging for troubleshooting.
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Ques 26. What is the purpose of Azure Data Factory managed private endpoints?
Managed private endpoints allow you to securely access data stores over a private connection, extending the data factory's network into the data store's virtual network.
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Ques 27. What is the purpose of data partitioning in Azure Data Factory?
Data partitioning is used to divide large datasets into smaller, more manageable partitions to improve processing efficiency and parallelism.
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Ques 28. How can you handle schema drift in Azure Data Factory?
Schema drift is handled through mapping data flow transformations that dynamically adjust to changes in the source or destination schema during data movement.
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Ques 29. What is the purpose of the Azure Data Factory Mapping Data Flow?
Mapping Data Flow is a visual data transformation feature in Azure Data Factory that allows you to design and execute complex data transformations using a graphical interface.
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Ques 30. Explain the concept of Azure Data Factory Managed Virtual Network.
Managed Virtual Network allows you to isolate the Azure Data Factory environment and control the network traffic for enhanced security and privacy.
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