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Top 100 Azure Data Factory Interview Questions and Answers

Top 100 Azure Data Factory Interview Questions and Answers

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1. What is Azure Data Factory (ADF)?

Azure Data Factory is a cloud-based data integration service used to create, schedule, and manage data pipelines. It allows you to move and transform data between different data stores and processing services.


2. How do you create a Linked Service in Azure Data Factory?

To create a linked service in ADF, you need to define a connection to a data store or a compute service. Here’s an example of creating an Azure Blob Storage linked service:

{
    "name": "MyAzureBlobStorageLinkedService",
    "properties": {
        "type": "AzureBlobStorage",
        "typeProperties": {
            "connectionString": "your_connection_string"
        }
    }
}

Official Documentation


3. What are Data Pipelines in Azure Data Factory?

A data pipeline in ADF is a logical grouping of activities that together perform a task. It defines the flow and transformation of data from source to destination.


4. How do you copy data from one Azure SQL Database to another using ADF?

You can use the Copy Data activity. Here’s an example JSON code snippet:

{
    "name": "CopyDataFromAzureSQLtoAzureSQL",
    "type": "Copy",
    "inputs": [{
        "referenceName": "SrcAzureSQLDataset"
    }],
    "outputs": [{
        "referenceName": "DestAzureSQLDataset"
    }],
    "typeProperties": {
        "source": {
            "type": "SqlSource"
        },
        "sink": {
            "type": "SqlSink"
        }
    }
}

Copy Activity


5. What is a Data Integration Runtime (DIR) in Azure Data Factory?

A Data Integration Runtime (DIR) is the compute infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments.

DIR Documentation


6. How do you handle errors in Azure Data Factory?

You can use activities like If Condition and Execute Pipeline with a custom error handling logic. Additionally, you can use Azure Functions or Stored Procedures for more complex error handling.

Error Handling Techniques


7. What is a Databricks Linked Service in Azure Data Factory?

A Databricks Linked Service is used to connect Azure Data Factory to an Azure Databricks workspace. It allows you to run Databricks notebooks as part of your ADF pipelines.

Databricks Linked Service Documentation


8. How do you parameterize a pipeline in Azure Data Factory?

You can define parameters at the pipeline level and reference them in activities. Here’s an example:

"parameters": {
    "sourcePath": {
        "type": "String",
        "defaultValue": "source/container/"
    }
}

Pipeline Parameters


9. What is Activity Dependency in Azure Data Factory?

Activity dependency defines the order in which activities should be executed within a pipeline. It ensures that one activity runs only after its dependent activities have successfully completed.

Activity Dependency Documentation


10. How do you monitor and manage Azure Data Factory pipelines?

You can use the Azure Data Factory Monitoring Hub to monitor pipeline runs, view metrics, and manage alerts. Additionally, you can use Azure Monitor for more advanced monitoring capabilities.

Monitoring Hub


11. How do you handle schema drift in Azure Data Factory?

Azure Data Factory supports automatic schema drift handling for many data stores. If automatic handling isn’t available, you can manually handle schema drift by using the Mapping Data Flows to dynamically map fields.

Schema Drift Handling


12. What is a Trigger in Azure Data Factory?

A trigger in Azure Data Factory is used to initiate the execution of a pipeline or a set of pipelines. It can be scheduled to run at specific times or triggered by events like a new file arriving in a storage account.

Trigger Documentation


13. How do you parameterize a dataset in Azure Data Factory?

You can define parameters at the dataset level and reference them in linked services or activities. This allows for dynamic configuration of dataset properties.

Dataset Parameters


14. What is Data Flows in Azure Data Factory?

Data Flows in Azure Data Factory provide a visual interface for building data transformation logic. They allow you to design ETL processes using a drag-and-drop interface.

Data Flows Documentation


15. How do you handle incremental loading in Azure Data Factory?

You can use watermark or date-time column-based filtering to identify new or updated records since the last load. This allows for incremental loading of data.

Incremental Loading Techniques


16. What is a Linked Service Dataset in Azure Data Factory?

A Linked Service Dataset is a dataset associated with a linked service. It defines the data structure and properties for the data source or sink.

Linked Service Dataset Documentation


17. How do you handle data encryption in Azure Data Factory?

You can configure linked services to use encrypted connections, and utilize Azure Key Vault to securely store and manage encryption keys.

Data Encryption in ADF


18. What is Data Masking in Azure Data Factory?

Data masking in Azure Data Factory involves obfuscating sensitive data within pipelines to protect privacy and confidentiality. It can be achieved using the Dynamic Data Masking transformation.

Dynamic Data Masking Documentation


19. How do you integrate Azure Data Factory with Azure DevOps?

You can use Azure DevOps for source control and automated deployment of Azure Data Factory pipelines. This allows for versioning and collaborative development.

Integration with Azure DevOps


20. What is the purpose of a Data Factory Integration Runtime?

A Data Factory Integration Runtime provides the compute infrastructure for Azure Data Factory. It’s used to move and transform data between data stores and compute services.

Integration Runtime Documentation


21. What is Azure Data Factory Data Flow Debugging?

Data Flow Debugging in Azure Data Factory allows you to test and troubleshoot data transformation logic within a data flow. It provides insights into how data is processed and allows for identifying and fixing errors.

Debug Data Flows


22. How do you schedule pipeline runs in Azure Data Factory?

You can use triggers to schedule pipeline runs at specific times or in response to events. Triggers can be configured to run once or on a recurring basis.

Scheduling Pipelines


23. What is Azure Data Factory Managed Virtual Network (VNet) Integration?

Managed VNet Integration in Azure Data Factory allows you to securely connect your data factory to resources in an Azure Virtual Network, like Azure SQL or Azure Synapse Analytics, using a VNet.

Managed VNet Integration Documentation


24. How do you monitor and manage Azure Data Factory pipelines using PowerShell?

You can use the Azure PowerShell module to manage and monitor Azure Data Factory pipelines. This allows for scripting and automation of ADF operations.

ADF PowerShell Cmdlets


25. What is the difference between Data Factory and Data Factory Data Flows?

Azure Data Factory is the overall service for building, scheduling, and managing data pipelines. Data Flows are a specific feature within Data Factory that provides a visual interface for building data transformation logic.

Data Flows vs Data Factory


26. How do you parameterize a Linked Service in Azure Data Factory?

You can define parameters at the linked service level and reference them in activities or datasets. This allows for dynamic configuration of linked service properties.

Linked Service Parameters


27. What is Azure Data Factory Mapping Data Flow?

Mapping Data Flow in Azure Data Factory is a visually designed data transformation process. It allows you to define data transformations using a drag-and-drop interface.

Mapping Data Flow Documentation


28. How do you use Azure Data Factory to ingest streaming data?

You can use the Stream Analytics source in Azure Data Factory to ingest streaming data from sources like Azure Event Hubs or IoT Hubs.

Ingesting Streaming Data


29. What is the purpose of Data Flow Debug Mode in Azure Data Factory?

Data Flow Debug Mode in Azure Data Factory allows you to interactively debug data flows by executing them step by step. It helps identify and resolve issues in your data transformation logic.

Data Flow Debug Mode


30. How do you deploy Azure Data Factory resources across multiple environments?

You can use Azure DevOps or ARM templates to automate the deployment of Azure Data Factory resources across different environments, like development, staging, and production.

Deployment Strategies


31. What is the purpose of a Data Factory Managed Identity?

A Data Factory Managed Identity allows Data Factory to authenticate to other Azure services without the need for storing credentials. It enhances security and simplifies access control.

Managed Identity Documentation


32. How do you parameterize a pipeline in Azure Data Factory?

You can define parameters at the pipeline level and reference them in activities. This enables dynamic configuration of pipeline properties.

Pipeline Parameters


33. What is Azure Data Factory Data Lake Storage Linked Service?

Azure Data Factory Data Lake Storage Linked Service is used to connect Data Factory to Azure Data Lake Storage Gen1 or Gen2. It allows for seamless movement of data between the two services.

Data Lake Storage Linked Service Documentation


34. How do you perform data validation in Azure Data Factory?

You can use activities like Data Flow or Stored Procedure to validate data. Additionally, you can use conditional activities to handle success or failure based on validation results.

Data Validation Techniques


35. What is a Self-Hosted Integration Runtime in Azure Data Factory?

A Self-Hosted Integration Runtime is a compute environment used to move data between on-premises and cloud environments. It allows Data Factory to access resources in your network.

Self-Hosted IR Documentation


36. How do you create a schedule-triggered pipeline in Azure Data Factory?

You can create a trigger that is set to run at a specific time or recurrence pattern. Attach this trigger to the desired pipeline to automate its execution.

Creating Triggers


37. What is the purpose of Data Factory Data Flows Debug Mode?

Debug Mode in Data Flows allows you to iteratively develop and test transformations. It provides a step-by-step execution for easier troubleshooting.

Data Flow Debug Mode


38. How do you handle sensitive information like credentials in Azure Data Factory?

You can use Azure Key Vault to securely store and retrieve sensitive information like passwords and API keys. Data Factory can then access these secrets at runtime.

Azure Key Vault Integration


39. What is the purpose of a Data Factory Data Flow Source?

A Data Flow Source defines the data source for a transformation. It specifies where the data comes from, like a file, database, or other service.

Data Flow Source Documentation


40. How do you create a Data Factory pipeline using Azure DevOps?

You can use Azure DevOps to manage your Data Factory pipelines by defining them as code in JSON files and using Git for version control.

Azure DevOps for Data Factory


41. What is Azure Data Factory Data Flow Sink?

A Data Flow Sink in Azure Data Factory defines the destination where data is written after processing. It specifies where the transformed data will be stored or sent.

Data Flow Sink Documentation


42. How do you monitor the execution of pipelines in Azure Data Factory?

Azure Data Factory provides monitoring capabilities through the ADF portal. You can view pipeline runs, monitor activities, and access logs for troubleshooting.

Monitoring Pipelines


43. What is Data Factory Linked Service JSON Definition?

Linked Service JSON Definition in Azure Data Factory is a JSON representation of a linked service’s configuration. It can be used for versioning and source control.

Linked Service JSON Documentation


44. How do you handle data partitioning in Azure Data Factory?

Data partitioning can be achieved using techniques like range partitioning or hash partitioning within the data transformation logic of a Data Flow.

Partitioning Techniques


45. What is a Data Factory Data Flow Aggregate Transformation?

The Aggregate Transformation in Data Flows is used to perform aggregate operations like sum, count, average, etc., on data. It is often used for summarizing data.

Aggregate Transformation Documentation


46. How do you handle slow-changing dimensions in Azure Data Factory?

You can use the Slow Changing Dimension Transformation in Data Flows to handle Type 1 and Type 2 slow-changing dimensions.

Handling Slow-Changing Dimensions


47. What is the purpose of a Data Factory Data Flow Derived Column Transformation?

The Derived Column Transformation allows you to create new columns or modify existing ones based on expressions. It’s useful for data cleansing and preparation.

Derived Column Transformation Documentation


48. How do you handle errors and exceptions in Azure Data Factory?

You can use error handling techniques like conditional activities and error outputs in Data Flows to manage exceptions during data processing.

Error Handling Techniques


49. What is the purpose of a Data Factory Data Flow Conditional Split Transformation?

The Conditional Split Transformation in Data Flows allows you to route data to different paths based on specified conditions. It’s useful for branching logic.

Conditional Split Transformation Documentation


50. How do you handle dynamic file names in Azure Data Factory?

You can use parameters and expressions in file paths to dynamically generate file names based on runtime values.

Dynamic File Names Documentation


51. What is Azure Data Factory Data Flow Window Transformation?

The Window Transformation in Data Flows is used for operations that require a sliding or tumbling window of data, like calculating moving averages or aggregations.

Window Transformation Documentation


52. How do you handle null values in Azure Data Factory Data Flows?

You can use conditional expressions to handle null values in Data Flow transformations, ensuring data integrity and accuracy.

Handling Null Values in Data Flows


53. What is the purpose of a Data Factory Data Flow Lookup Transformation?

The Lookup Transformation in Data Flows allows you to perform lookups on a reference dataset, enriching or transforming your data based on matching conditions.

Lookup Transformation Documentation


54. How do you use parameters in Azure Data Factory Data Flows?

Parameters in Data Flows can be used to make your data transformation logic more dynamic and reusable across different scenarios.

Using Parameters in Data Flows


55. What is Azure Data Factory Data Flow Surrogate Key Transformation?

The Surrogate Key Transformation in Data Flows is used to generate and assign surrogate keys to records, ensuring unique identification.

Surrogate Key Transformation Documentation


56. How do you handle complex data structures in Azure Data Factory Data Flows?

You can use techniques like nested mapping and structured transformations to handle complex data structures in Data Flows.

Handling Complex Data Structures


57. What is Azure Data Factory Data Flow Pivot Transformation?

The Pivot Transformation in Data Flows is used to rotate or transpose data, changing the orientation of rows and columns.

Pivot Transformation Documentation


58. How do you use parameters in Azure Data Factory pipeline activities?

Parameters in pipeline activities allow for dynamic configuration of activity properties, enhancing reusability and flexibility.

Using Parameters in Activities


59. What is the purpose of a Data Factory Integration Runtime Auto-Resolve Integration Runtime?

Auto-Resolve Integration Runtime in Data Factory automatically selects an integration runtime based on the location and capabilities of the source and sink.

Auto-Resolve IR Documentation


60. How do you create a Data Factory Linked Service for Azure Synapse Analytics?

You can create a Linked Service for Azure Synapse Analytics to establish a connection between Data Factory and Synapse for data movement and transformation.

Linked Service for Synapse Documentation


61. What is the purpose of a Data Factory Data Flow Select Transformation?

The Select Transformation in Data Flows allows you to choose specific columns from a dataset, enabling you to focus on relevant data for further processing.

Select Transformation Documentation


62. How do you handle incremental data loading in Azure Data Factory?

You can use techniques like watermarking or change tracking to identify and extract only the changed or new records for incremental data loads.

Incremental Data Loading Techniques


63. What is Azure Data Factory Data Flow Source Output Distribution?

Source Output Distribution defines how data is divided across partitions for parallel processing. It’s important for optimizing Data Flow performance.

Source Output Distribution Documentation


64. How do you handle slowly changing dimensions (Type 2) in Azure Data Factory?

You can use the Slow Changing Dimension Transformation with the appropriate settings to handle Type 2 slowly changing dimensions in Data Flows.

Handling Type 2 SCD in Data Flows


65. What is a Data Factory Data Flow Filter Transformation?

The Filter Transformation in Data Flows allows you to apply conditions to filter out records that meet specific criteria.

Filter Transformation Documentation


66. How do you use a Data Factory Lookup Activity?

The Lookup Activity in Data Factory is used to retrieve a dataset from a specified source, which can be used in subsequent pipeline activities.

Lookup Activity Documentation


67. What is Azure Data Factory Data Flow Join Transformation?

The Join Transformation in Data Flows combines two or more datasets based on a specified condition, allowing for data consolidation.

Join Transformation Documentation


68. How do you handle schema drift in Azure Data Factory Data Flows?

You can use the Auto-Mapping feature in Data Flows to automatically map source and sink columns, even if they have different schemas.

Handling Schema Drift


69. What is the purpose of a Data Factory Data Flow Union Transformation?

The Union Transformation in Data Flows combines multiple datasets with the same schema into a single dataset, facilitating data aggregation.

Union Transformation Documentation


70. How do you handle data skew in Azure Data Factory Data Flows?

You can use techniques like partitioning, parallelization, and optimizing transformations to address data skew issues in Data Flows.

Handling Data Skew


71. What is Azure Data Factory Data Flow Source Output Partitioning?

Source Output Partitioning defines how the source data is divided across partitions for parallel processing. It’s crucial for optimizing Data Flow performance.

Source Output Partitioning Documentation


72. How do you handle complex data transformations in Azure Data Factory Data Flows?

You can use custom expressions and scripts in Data Flow transformations to handle complex data processing and transformations.

Complex Data Transformations Documentation


73. What is the purpose of a Data Factory Data Flow Rank Transformation?

The Rank Transformation in Data Flows assigns a rank to each record based on specified criteria. It’s useful for identifying top or bottom records.

Rank Transformation Documentation


74. How do you use stored procedures in Azure Data Factory Data Flows?

You can use the Execute Stored Procedure transformation to invoke stored procedures within Data Flows for advanced data processing.

Using Stored Procedures in Data Flows


75. What is Azure Data Factory Data Flow Window Frame Specification?

The Window Frame Specification in Data Flows defines the set of rows used in window functions. It’s essential for performing advanced analytical operations.

Window Frame Specification Documentation


76. How do you handle data validation and cleansing in Azure Data Factory Data Flows?

You can use Data Flow expressions and transformations to perform data validation, cleansing, and enrichment operations.

Data Validation and Cleansing Techniques


77. What is Azure Data Factory Data Flow Aggregate Window Function?

The Aggregate Window Function in Data Flows allows you to perform aggregate operations over a specified window of rows.

Aggregate Window Function Documentation


78. How do you handle late-arriving data in Azure Data Factory?

You can use techniques like watermarking, windowing, and buffering to handle late-arriving data in Data Flows.

Handling Late-Arriving Data


79. What is the purpose of a Data Factory Data Flow Conditional Split Transformation?

The Conditional Split Transformation in Data Flows allows you to route data to different paths based on specified conditions. It’s useful for branching logic.

Conditional Split Transformation Documentation


80. How do you use a Data Factory Web Activity?

The Web Activity in Data Factory is used to call a REST endpoint or a web service, enabling integration with external APIs and services.

Web Activity Documentation


81. What is the purpose of a Data Factory Data Flow Derived Column Transformation?

The Derived Column Transformation in Data Flows allows you to create new columns or modify existing ones based on expressions or functions.

Derived Column Transformation Documentation


82. How do you handle dynamic file names in Azure Data Factory?

You can use dynamic content and expressions in the dataset properties to generate dynamic file names in Data Factory.

Handling Dynamic File Names


83. What is Azure Data Factory Data Flow Data Preview?

Data Preview in Data Flows allows you to preview a sample of the transformed data before executing the Data Flow.

Data Preview Documentation


84. How do you handle errors and exceptions in Azure Data Factory Data Flows?

You can use error handling techniques like conditional expressions and error output paths to manage exceptions in Data Flows.

Handling Errors in Data Flows


85. What is a Data Factory Data Flow Foreach Activity?

The Foreach Activity in Data Factory allows you to iterate over a collection and perform a set of activities for each item.

Foreach Activity Documentation


86. How do you handle schema evolution in Azure Data Factory Data Flows?

You can use techniques like schema drift handling and dynamic mappings to accommodate evolving schemas in Data Flows.

Handling Schema Evolution


87. What is Azure Data Factory Data Flow Source Query?

Source Query in Data Flows allows you to specify a custom query to retrieve data from the source, providing more control over data extraction.

Source Query Documentation


88. How do you handle data encryption in Azure Data Factory?

You can use encrypted connections and managed identities to ensure secure data movement and processing in Data Factory.

Data Encryption Techniques


89. What is the purpose of a Data Factory Data Flow Conditional Split on Error Transformation?

The Conditional Split on Error Transformation in Data Flows allows you to route error records to different paths for specific handling.

Conditional Split on Error Transformation Documentation


90. How do you use a Data Factory Lookup Range Start Activity?

The Lookup Range Start Activity in Data Factory is used to initiate a loop based on a specified range of values.

Lookup Range Start Activity Documentation


91. How do you handle timezone conversions in Azure Data Factory?

You can use functions like utcnow() and convertTimeZone() in expressions to handle timezone conversions in Data Factory.

Handling Timezone Conversions


92. What is the purpose of a Data Factory Data Flow Surrogate Key Transformation?

The Surrogate Key Transformation in Data Flows generates unique identifiers for records, typically used in data warehousing scenarios.

Surrogate Key Transformation Documentation


93. How do you handle complex data validation rules in Azure Data Factory Data Flows?

You can use conditional expressions and custom validation logic in Data Flows to enforce complex data quality rules.

Handling Complex Data Validation


94. What is Azure Data Factory Data Flow Source Join?

The Source Join in Data Flows allows you to perform join operations between multiple datasets, combining them into a single dataset.

Source Join Documentation


95. How do you handle data masking and anonymization in Azure Data Factory?

You can use techniques like custom expressions and external services to mask or anonymize sensitive data in Data Flows.

Data Masking Techniques


96. What is the purpose of a Data Factory Data Flow Aggregate Transformation?

The Aggregate Transformation in Data Flows allows you to perform aggregate operations like sum, average, count, etc., on groups of data.

Aggregate Transformation Documentation


97. How do you use a Data Factory Lookup Range End Activity?

The Lookup Range End Activity in the Data Factory is used to mark the end of a loop initiated by the Lookup Range Start Activity.

Lookup Range End Activity Documentation


98. What is Azure Data Factory Data Flow Data Preview Mode?

Data Preview Mode in Data Flows allows you to interactively explore and validate the data transformation logic before execution.

Data Preview Mode Documentation


99. How do you handle row-level security in Azure Data Factory Data Flows?

You can use filtering and conditional logic to implement row-level security policies in Data Flows.

Row-Level Security Techniques


100. What is Azure Data Factory Data Flow Cache?

Data Flow Cache is a feature that allows you to cache intermediate results to improve performance in Data Flows.

Data Flow Cache Documentation