Azure Data Factory Interview Questions and Answers for freshers

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Azure Data Factory Interview Questions for Freshers

  1. What is Azure Data Factory (ADF)?

    • Answer: Azure Data Factory is a fully managed, cloud-based ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) service that allows you to create, schedule, and monitor data pipelines. It enables you to ingest, transform, and move data between various data stores, including on-premises, cloud, and SaaS-based systems.

  2. What are the key components of an Azure Data Factory pipeline?

    • Answer: Key components include datasets (representing data sources and sinks), linked services (connections to data stores), activities (transformations and data movement operations), pipelines (workflows orchestrating activities), triggers (scheduling and triggering pipelines), and monitoring tools.

  3. Explain the difference between a dataset and a linked service in ADF.

    • Answer: A linked service defines the connection to a data store (e.g., SQL Server, Azure Blob Storage), including credentials and connection details. A dataset represents a specific data asset within a linked service, defining its location, structure, and format (e.g., a specific table in a SQL Server database or a CSV file in Azure Blob Storage).

  4. What are different types of activities available in ADF?

    • Answer: ADF offers a wide range of activities, including data movement activities (Copy Data, Web activity), data transformation activities (Data Flow, Lookup, Stored Procedure), control flow activities (For Each, If Condition), and other miscellaneous activities (Email, Execute Pipeline).

  5. How do you handle errors in ADF pipelines?

    • Answer: Error handling involves using try-catch blocks within activities, defining retry policies for activities, setting up alerts based on pipeline failures, and leveraging monitoring tools to identify and troubleshoot errors. ADF also offers built-in mechanisms for managing exceptions and logging.

  6. Explain the concept of Data Flows in ADF.

    • Answer: Data Flows provide a visual, code-free way to perform data transformations within ADF. They leverage a graphical interface to define transformations using various operators like select, join, aggregate, and filter. Data Flows are optimized for performance and scalability.

  7. What are different types of triggers in ADF?

    • Answer: ADF supports various triggers including schedule triggers (for recurring execution), tumbling window triggers (for processing data in batches), and event-based triggers (triggered by events in other Azure services).

  8. How do you monitor and debug ADF pipelines?

    • Answer: ADF provides monitoring tools to track pipeline execution, view activity logs, identify failures, and analyze performance. Debugging can involve stepping through pipelines, examining activity outputs, and using the monitoring tools to troubleshoot issues.

  9. What are self-hosted integration runtimes in ADF?

    • Answer: Self-hosted integration runtimes are on-premises or virtual machines that enable ADF to connect to and process data from on-premises data stores which are not directly accessible from the cloud.

  10. Explain the concept of global parameters in ADF.

    • Answer: Global parameters are variables that can be defined at the data factory level and reused across multiple pipelines and datasets. This promotes reusability and simplifies pipeline management.

  11. How do you manage version control in ADF?

    • Answer: ADF integrates with Azure DevOps and Git for version control. This enables collaborative development, code review, and rollback capabilities.

  12. What are some best practices for designing ADF pipelines?

    • Answer: Best practices include modular pipeline design, using appropriate data transformations, implementing error handling, using global parameters for reusability, and monitoring pipeline performance.

  13. How does ADF handle large datasets?

    • Answer: ADF uses parallel processing and optimized data movement techniques to efficiently handle large datasets. It also supports partitioning and sharding to improve performance.

  14. What are some common performance tuning techniques for ADF pipelines?

    • Answer: Techniques include optimizing data transformations, using appropriate data formats, partitioning datasets, and utilizing parallel processing.

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