50 latest interview questions for Azure Data Factory
Latest Interview questions
What is Azure Data Factory (ADF)?
Answer: Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines to move data from various sources to destinations.
Explain the key components of Azure Data Factory.
Answer: ADF includes datasets, linked services, data pipelines, and activities. Datasets represent data structures, linked services connect to data sources, data pipelines define data movement, and activities are specific data operations.
What is a linked service in Azure Data Factory?
Answer: A linked service is a connection to an external data source or destination. It contains connection information and credentials to access the data.
What is a data pipeline in Azure Data Factory?
Answer: A data pipeline is a logical grouping of data activities that together perform a task, such as data movement, transformation, or orchestration.
Explain the difference between Data Flow and Control Flow in ADF.
Answer: Data Flow is responsible for data transformations, while Control Flow manages the execution and sequencing of activities in a pipeline.
What is a dataset in Azure Data Factory?
Answer: A dataset represents the structure of data to be used in a pipeline. It defines the schema, format, and location of the data.
How do you trigger a pipeline execution in ADF?
Answer: Pipelines can be triggered manually, on a schedule, or in response to an event using triggers.
What is a trigger in Azure Data Factory?
Answer: A trigger is a mechanism to initiate the execution of a pipeline. It can be time-based (schedule) or event-based (e.g., when a file arrives).
Explain Data Movement activities in ADF.
Answer: Data Movement activities copy data from source to destination. Azure Data Factory supports various data movement activities like Copy Data, Data Flow, and Data Flow Debug.
What is Azure Data Factory Data Flow?
Answer: Data Flow is a transformation activity in ADF used for data wrangling and transformation using a visual interface.
How does ADF handle data consistency during data movement?
Answer: ADF uses checkpoints to ensure data consistency during data movement. It tracks the progress of each copy activity and can resume from the last checkpoint in case of a failure.
What is Azure Integration Runtime in ADF?
Answer: Azure Integration Runtime is the compute infrastructure used by Azure Data Factory to execute data integration tasks.
Explain the concept of Data Lake Storage Gen2 in ADF.
Answer: Data Lake Storage Gen2 is a scalable and secure data lake solution integrated with ADF, providing a centralized repository for structured and unstructured data.
What are the supported execution triggers for ADF pipelines?
Answer: ADF pipelines can be triggered manually, on a schedule, or by external events using event-based triggers.
What is the difference between a pipeline parameter and a system variable in ADF?
Answer: Pipeline parameters are user-defined values passed to a pipeline, while system variables are predefined by ADF and provide information about the pipeline's runtime environment.
Explain the concept of Azure DevOps integration with Azure Data Factory.
Answer: Azure DevOps integration allows you to manage, version control, and automate the deployment of ADF resources using DevOps practices and CI/CD pipelines.
How can you monitor and manage Azure Data Factory pipelines?
Answer: You can use Azure Monitor, Azure Data Factory's built-in monitoring, and logging capabilities to track pipeline runs, activity statuses, and performance.
What is Data Factory Managed Virtual Network Integration?
Answer: It enables Azure Data Factory to securely connect to on-premises data sources and destinations by leveraging Azure Virtual Network.
Explain the concept of Data Factory self-hosted integration runtime.
Answer: It allows ADF to connect to on-premises data sources and destinations by installing a runtime on your own infrastructure.
How do you handle schema changes in the source data in ADF?
Answer: You can use schema drift settings in ADF to handle schema changes in source data by allowing ADF to adapt dynamically to changes.
What are data slices in Azure Data Factory?
Answer: Data slices are time-based partitions of data used in time-series data processing scenarios, often associated with scheduled data pipelines.
What is Azure Data Factory Mapping Data Flow Debug?
Answer: It's a feature that allows you to debug data flows in ADF visually, making it easier to identify and fix data transformation issues.
How can you handle errors in Azure Data Factory pipelines?
Answer: ADF provides error handling mechanisms such as retry policies, fault tolerance, and error outputs in data flows.
Explain the concept of Data Factory Triggers and their types.
Answer: Triggers in ADF are used to automate pipeline execution. Types include Schedule, Event, and Tumbling Window triggers.
What is Data Factory Data Flow Debug mode used for?
Answer: It allows developers to test and debug data transformations within data flows before deploying them in a pipeline.
What is a managed private endpoint in Azure Data Factory?
Answer: It enables secure access to data stored in Azure using private IP addresses and eliminates data exposure to the public internet.
Explain the differences between Azure Data Factory and Azure SQL Data Warehouse.
Answer: ADF is a data integration service for moving and transforming data, while Azure SQL Data Warehouse is a fully managed data warehousing service for analytics.
What is Data Factory data masking?
Answer: Data masking is a feature in ADF that allows you to apply data obfuscation techniques to protect sensitive information during data movement.
How do you parameterize linked services and datasets in Azure Data Factory?
Answer: You can use parameterization to make linked service and dataset configurations dynamic, allowing for flexibility in pipeline design.
What is the purpose of Data Factory data flow dynamic content expressions?
Answer: Dynamic content expressions are used to derive values dynamically at runtime, making it possible to create flexible and adaptive data flows.
Explain the concept of parameterization in Azure Data Factory.
Answer: Parameterization allows you to externalize values in ADF resources, making it easier to reuse and maintain pipelines and datasets.
What is the difference between Azure Data Factory and Azure Logic Apps?
Answer: Azure Data Factory focuses on data integration and transformation, while Azure Logic Apps are used for workflow automation and integration between services.
Can you run Azure Data Factory pipelines on a specific node or machine?
Answer: No, ADF is a fully managed service, and you cannot control the specific nodes or machines where pipelines run.
What is a linked service parameter in Azure Data Factory?
Answer: A linked service parameter is a parameterized linked service that allows you to dynamically change the linked service connection details at runtime.
Explain the concept of Data Factory service identity.
Answer: Service identity in ADF enables secure access to Azure resources and helps control permissions and authentication for data movement.
What is the maximum retention period for monitoring data in Azure Data Factory?
Answer: The maximum retention period for monitoring data in ADF is 31 days.
How can you schedule a pipeline to run at specific times with Azure Data Factory?
Answer: You can use the schedule trigger type to specify the exact times when a pipeline should run.
What is Data Factory parameter passing, and why is it important?
Answer: Parameter passing allows you to pass values between activities and data flows in a pipeline, making it possible to create dynamic and reusable pipelines.
Explain how you can monitor and troubleshoot Azure Data Factory pipeline failures.
Answer: You can use the ADF monitoring and logging features to identify the root cause of pipeline failures, view error details, and take corrective actions.
What is the purpose of the "ForEach" activity in Azure Data Factory?
Answer: The "ForEach" activity is used to iterate over a collection and execute a series of activities for each item in the collection.
Can you use custom Python or .NET code within Azure Data Factory pipelines?
Answer: Yes, you can use custom code activities to run Python or .NET code within ADF pipelines.
What is the difference between "Delete" and "Terminate" in the context of pipeline execution in ADF?
Answer: "Delete" removes a pipeline run record, while "Terminate" stops the execution of a running pipeline.
Explain the concept of mapping data flow in Azure Data Factory.
Answer: Mapping data flow is a visual, code-free interface for designing data transformations and data wrangling operations within ADF.
How can you secure sensitive data in Azure Data Factory pipelines?
Answer: You can use Azure Key Vault integration to store and manage sensitive data securely, such as connection strings and credentials.
What is the purpose of triggers in Azure Data Factory?
Answer: Triggers automate the execution of pipelines, allowing you to schedule them at specific times or trigger them in response to events.
How do you parameterize a Data Factory pipeline with ADF managed parameters?
Answer: You can define parameters at the pipeline level and then use them within the activities and datasets of the pipeline.
What is Data Factory data flow source transformation used for?
Answer: The source transformation in Data Flow is used to read data from sources such as files, databases, or REST APIs.
Explain the use of Data Factory Lookup activity.
Answer: The Lookup activity retrieves data from a specified dataset and allows you to use that data in subsequent activities or transformations.
What is Azure Data Factory Integration Runtimes and how does it affect data movement?
Answer: Azure Data Factory Integration Runtimes determine the location where data movement and transformation activities are executed. It can be Azure, Self-hosted, or SSIS Integration Runtimes.
These questions and answers provide a good foundation for interviewing candidates about Azure Data Factory.