Azure Data Factory Interview Questions and Answers
Intermediate / 1 to 5 years experienced level questions & answers
Ques 1. Explain the key components of Azure Data Factory.
Key components include datasets, linked services, pipelines, activities, and data flows.
Ques 2. 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.
Ques 3. 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.
Ques 4. 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.
Ques 5. What is Azure Data Factory Integration Runtimes?
Integration Runtimes define the compute infrastructure used by data factory for data movement and data transformation activities.
Ques 6. 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.
Ques 7. 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.
Ques 8. 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.
Ques 9. 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.
Ques 10. 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.
Ques 11. 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.
Ques 12. 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.
Ques 13. 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.
Ques 14. 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.
Ques 15. 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.
Ques 16. 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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