Are you preparing for ADF interviews? To help you out we’ve compiled a comprehensive list of over 25 ADF interviews scenario-based questions with detailed answers. These questions cover a wide range of topics, from pipeline design and data transformation to integration with other services like Databricks and real-time data processing. Whether you’re a seasoned professional or just starting out, this guide will equip you with the knowledge and insights needed to confidently tackle any ADF-related interview.
Top 25+ ADF interview questions scenario based questions with detailed answers
- Scenario: Your organization needs to migrate on-premises SQL Server data to Azure SQL Database using ADF. How would you design this pipeline?
- Scenario: You need to process and transform 1 TB of CSV data stored in Azure Blob Storage before loading it into Azure Data Lake Storage. What approach would you take?
- Scenario: Your data pipeline must execute only when a new file arrives in Azure Blob Storage. How would you configure this in ADF?
- Scenario: You need to perform incremental data loading from an on-premises Oracle database to Azure SQL Database. How would you implement this in ADF?
- Scenario: Your pipeline must execute a Databricks notebook for data transformation. How would you integrate Databricks into your ADF pipeline?
- Scenario: You need to copy data from multiple sources to a single destination in parallel. How would you design this in ADF?
- Scenario: Your pipeline must handle failures gracefully and retry operations upon transient errors. How would you implement this in ADF?
- Scenario: You need to execute a stored procedure in an Azure SQL Database as part of your pipeline. How would you configure this in ADF?
- Scenario: You need to process data from multiple files in Azure Blob Storage, each with a different schema, and load them into a single Azure SQL Database table. How would you handle this in Azure Data Factory (ADF)?
- Scenario: Your organization requires real-time data processing for streaming data from IoT devices into Azure Data Lake Storage. How would you set up this pipeline in ADF?
- Scenario: You need to implement a data pipeline that runs daily but skips weekends and public holidays. How would you configure this schedule in ADF?
- Scenario: Your data processing requires executing Python scripts for complex transformations. How would you integrate these scripts into your ADF pipeline?
- Scenario: You need to copy data from an FTP server to Azure Blob Storage, but the FTP server allows connections only from specific IP addresses. How would you configure this in ADF?
- Scenario: Your pipeline processes large datasets, and you need to optimize performance to reduce execution time. What strategies would you employ in ADF?
- Scenario: You need to implement a pipeline that processes data differently based on the day of the week. How would you design this in ADF?
- Scenario: Your organization requires data to be copied from Azure SQL Database to an on-premises SQL Server daily. How would you set up this pipeline in ADF?
- Scenario: Your organization needs to integrate data from multiple on-premises data sources into Azure Data Lake Storage for centralized analytics. How would you design this pipeline in ADF?
- Scenario: You need to implement a data pipeline that performs data validation before loading data into Azure SQL Database. How would you configure this in ADF?
- Scenario: Your pipeline must process data files that arrive at irregular intervals in Azure Blob Storage. How would you configure ADF to handle this scenario?
- Scenario: You need to implement a pipeline that merges data from two different Azure SQL Databases into a single table in Azure Synapse Analytics. How would you approach this in ADF?
- Scenario: Your organization requires a pipeline that extracts data from a REST API and loads it into Azure Cosmos DB. How would you set this up in ADF?
- Scenario: You need to implement a pipeline that processes data differently based on the file type (e.g., CSV, JSON) in Azure Blob Storage. How would you design this in ADF?
- Scenario: Your pipeline must load data into a partitioned table in Azure SQL Data Warehouse. How would you configure this in ADF?
- Scenario: You need to implement a pipeline that reads data from an SAP system and loads it into Azure Data Lake Storage. How would you set this up in ADF?
- Scenario: Your organization requires a data pipeline that ingests data from multiple SAP systems into Azure Data Lake Storage for centralized analytics. How would you design this pipeline in Azure Data Factory (ADF)?
- Scenario: Your organization requires a pipeline that extracts data from a REST API and loads it into Azure Cosmos DB. How would you set this up in ADF?
- Scenario: Your pipeline must load data into a partitioned table in Azure SQL Data Warehouse. How would you configure this in ADF?
- Scenario: You need to implement a pipeline that reads data from an SAP system and loads it into Azure Data Lake Storage. How would you set this up in ADF?
Scenario 1: Your organization needs to migrate on-premises SQL Server data to Azure SQL Database using ADF. How would you design this pipeline?
Answer: To migrate on-premises SQL Server data to Azure SQL Database using ADF, follow these steps:
- Integration Runtime: Set up a Self-hosted Integration Runtime (IR) on-premises to enable secure data movement between the on-premises environment and Azure.
- Linked Services: Create linked services for both the on-premises SQL Server and the Azure SQL Database, specifying connection details and credentials.
- Datasets: Define datasets for the source (on-premises SQL Server) and sink (Azure SQL Database) to represent the data structures involved.
- Pipeline and Activities: Design a pipeline incorporating a Copy Activity to transfer data from the source to the sink. Configure the activity to use the Self-hosted IR for the source and the Azure IR for the destination.
- Execution and Monitoring: Execute the pipeline and monitor its progress using ADF’s monitoring tools to ensure successful data migration.
Scenario 2: You need to process and transform 1 TB of CSV data stored in Azure Blob Storage before loading it into Azure Data Lake Storage. What approach would you take?
Answer: Processing and transforming large datasets efficiently can be achieved using ADF’s Mapping Data Flows:
- Mapping Data Flows: Utilize ADF’s Mapping Data Flows to design a data transformation logic visually. This allows for scalable data processing without writing code.
- Integration Runtime: Ensure that the Azure Integration Runtime is configured to handle the compute resources required for processing large datasets.
- Pipeline Configuration: Create a pipeline that includes the Mapping Data Flow activity, specifying the source as Azure Blob Storage and the sink as Azure Data Lake Storage.
- Execution and Monitoring: Run the pipeline and monitor its execution to ensure data is transformed and loaded correctly.
Scenario 3: Your data pipeline must execute only when a new file arrives in Azure Blob Storage. How would you configure this in ADF?
Answer: To trigger a pipeline upon the arrival of a new file in Azure Blob Storage:
- Event-Based Trigger: Set up an Event-Based Trigger in ADF that listens for blob creation events in the specified storage container.
- Trigger Configuration: Configure the trigger to initiate the pipeline whenever a new file is detected, ensuring that the pipeline processes the new data accordingly.
- Pipeline Design: Design the pipeline to handle the incoming data, including any necessary transformations or data movements.
Scenario 4: You need to perform incremental data loading from an on-premises Oracle database to Azure SQL Database. How would you implement this in ADF?
Answer: Implementing incremental data loading involves:
Watermarking: Maintain a watermark value (e.g., a timestamp or ID) to track the last successfully loaded record.
Pipeline Design: Create a pipeline that includes:
- Lookup Activity: Retrieve the current watermark value.
- Copy Activity: Use the watermark to filter source data for new or updated records.
- Stored Procedure Activity: Update the watermark value after successful data load.
Integration Runtime: Use a Self-hosted IR to connect to the on-premises Oracle database and an Azure IR for the Azure SQL Database.
Scenario 5: Your pipeline must execute a Databricks notebook for data transformation. How would you integrate Databricks into your ADF pipeline?
Answer: To integrate Databricks into an ADF pipeline:
- Linked Service: Create a linked service in ADF that connects to your Azure Databricks workspace, providing necessary authentication details.
- Notebook Activity: Add a Databricks Notebook activity to your pipeline, specifying the notebook path and any required parameters.
- Pipeline Execution: When the pipeline runs, it will trigger the Databricks notebook to perform the specified data transformations.
Scenario 6: You need to copy data from multiple sources to a single destination in parallel. How would you design this in ADF?
Answer: To copy data from multiple sources to a single destination in parallel:
- ForEach Activity: Use a ForEach activity in your pipeline to iterate over a list of data sources.
- Parallel Execution: Configure the ForEach activity to execute iterations in parallel by setting the “Batch Count” property to the desired level of concurrency.
- Copy Activity: Within the ForEach activity, include a Copy Activity that handles the data transfer from each source to the destination.
Scenario 7: Your pipeline must handle failures gracefully and retry operations upon transient errors. How would you implement this in ADF?
Answer: To handle failures and implement retry logic:
- Activity Retry Policy: Configure the retry policy for activities by setting properties such as “Retry,” “Retry Interval,” and “Retry Timeout” to define the number of retry attempts and intervals between them.
- Error Handling: Use activities like “If Condition” and “Until” to implement custom error handling and retry logic within the pipeline.
- Alerts and Monitoring: Set up alerts to notify stakeholders of failures that persist after all retry attempts.
Scenario 8: You need to execute a stored procedure in an Azure SQL Database as part of your pipeline. How would you configure this in ADF?
Answer: To execute a stored procedure within an Azure SQL Database as part of your ADF pipeline, follow these steps:
Create a Linked Service to Azure SQL Database:
- In ADF, navigate to the “Manage” section and select “Linked Services.”
- Click on “New” and choose “Azure SQL Database.”
- Provide the necessary connection details, including server name, database name, and authentication method.
- Test the connection to ensure it’s successful.
Add a Stored Procedure Activity to the Pipeline:
- In the “Author” section, create or edit a pipeline.
- Drag and drop the “Stored Procedure” activity onto the pipeline canvas.
Configure the Stored Procedure Activity:
- In the activity’s settings, select the linked service created in step 1.
- Choose the desired stored procedure from the dropdown list.
- If the stored procedure requires parameters, provide the necessary values.
Publish and Trigger the Pipeline:
- After configuring the activity, publish the pipeline.
- Trigger the pipeline manually or set up a schedule as needed.
This setup allows ADF to execute the specified stored procedure within your Azure SQL Database as part of the pipeline workflow.
Scenario 9: You need to process data from multiple files in Azure Blob Storage, each with a different schema, and load them into a single Azure SQL Database table. How would you handle this in Azure Data Factory (ADF)?
Answer: To process multiple files with varying schemas and load them into a single Azure SQL Database table using ADF, follow these steps:
- Schema Mapping: Utilize ADF’s Mapping Data Flows to define transformations that standardize the differing schemas into a unified format compatible with the destination table.
- Parameterization: Implement parameters within the pipeline to dynamically handle different file names and paths, allowing the pipeline to process each file accordingly.
- Control Flow: Incorporate a ForEach activity to iterate over the list of files in the Blob Storage. Within this loop, use a Copy Activity or Data Flow to process and load each file into the Azure SQL Database.
- Error Handling: Implement error handling mechanisms to manage any discrepancies or issues that arise due to schema differences during the data processing.
Scenario 10: Your organization requires real-time data processing for streaming data from IoT devices into Azure Data Lake Storage. How would you set up this pipeline in ADF?
Answer: To set up a real-time data processing pipeline for streaming IoT data into Azure Data Lake Storage using ADF:
- Event Hub Integration: Configure Azure Event Hubs to ingest streaming data from IoT devices.
- Stream Analytics: Set up Azure Stream Analytics to process the streaming data in real-time. Define input from Event Hubs and output to Azure Data Lake Storage.
- ADF Integration: While ADF is primarily designed for batch processing, you can orchestrate the setup by scheduling and monitoring the Stream Analytics jobs within ADF pipelines.
- Monitoring: Use ADF’s monitoring capabilities to oversee the health and performance of the Stream Analytics jobs, ensuring continuous data flow.
Scenario 11: You need to implement a data pipeline that runs daily but skips weekends and public holidays. How would you configure this schedule in ADF?
Answer: To configure a pipeline that runs daily, excluding weekends and public holidays:
- Schedule Trigger: Set up a Schedule Trigger to initiate the pipeline on weekdays (Monday through Friday).
- Custom Calendar: For public holidays, create a custom calendar or list that specifies these dates.
- Pipeline Logic: Incorporate a Lookup Activity at the beginning of the pipeline to check the current date against the custom calendar. Use an If Condition activity to determine whether to proceed with the pipeline execution or skip it based on the result.
Scenario 12: Your data processing requires executing Python scripts for complex transformations. How would you integrate these scripts into your ADF pipeline?
Answer: To integrate Python scripts for complex data transformations within an ADF pipeline:
- Azure Batch Service: Set up Azure Batch to run Python scripts. Create a pool of compute nodes and configure job scheduling.
- Custom Activity: In ADF, add a Custom Activity that references the Azure Batch linked service. Configure the activity to execute the Python script, passing any necessary parameters.
- Data Flow: Alternatively, if the transformation logic can be implemented using ADF’s Mapping Data Flows, consider using them to avoid external dependencies.
Scenario 13: You need to copy data from an FTP server to Azure Blob Storage, but the FTP server allows connections only from specific IP addresses. How would you configure this in ADF?
Answer: To copy data from an FTP server with IP restrictions to Azure Blob Storage:
- Self-hosted Integration Runtime (IR): Install a Self-hosted IR on a machine with an allowed IP address. This IR will facilitate the secure transfer of data from the FTP server.
- Linked Services: Create linked services in ADF for both the FTP server and Azure Blob Storage, associating the Self-hosted IR with the FTP linked service.
- Pipeline Configuration: Design a pipeline with a Copy Activity that uses the configured linked services to transfer data from the FTP server to Azure Blob Storage.
Scenario 14: Your pipeline processes large datasets, and you need to optimize performance to reduce execution time. What strategies would you employ in ADF?
Answer: To optimize performance for processing large datasets in ADF:
- Parallelism: Enable parallel execution in activities like Copy Activity by setting the “Degree of Copy Parallelism” property.
- Data Partitioning: Partition the data to allow concurrent processing of smaller chunks, reducing overall execution time.
- Integration Runtime Scaling: Scale up or out the Integration Runtime to provide more compute resources for data processing tasks.
- Efficient Data Movement: Use staging mechanisms or direct data paths to minimize data movement and latency.
Scenario 15: You need to implement a pipeline that processes data differently based on the day of the week. How would you design this in ADF?
Answer: To design a pipeline that processes data differently based on the day of the week:
- System Variables: Utilize the system variable
@{utcnow('dddd')}to retrieve the current day of the week. - If Condition Activity: Incorporate an If Condition activity to evaluate the day of the week and branch the pipeline execution accordingly.
- Branching: Within each branch, define the specific activities or transformations required for that particular day.
Scenario 16: Your organization requires data to be copied from Azure SQL Database to an on-premises SQL Server daily. How would you set up this pipeline in ADF?
Answer: To set up a daily data transfer from Azure SQL Database to an on-premises SQL Server, follow these steps:
Install Self-hosted Integration Runtime (IR):
- Install and configure IR on an on-premises machine and link it to ADF.
Create Linked Services:
- Azure SQL Database: Set up a linked service with connection details.
- On-Premises SQL Server: Use the Self-hosted IR and provide authentication details.
Define Datasets:
- Source: Configure Azure SQL Database dataset with the table or query.
- Sink: Set up on-premises SQL Server dataset with the target table.
Design Pipeline:
- Add a “Copy Data” activity, map the source (Azure SQL) to the sink (on-premises SQL), and configure any required transformations.
Set Up Schedule:
- Create a daily trigger to automate the pipeline.
Publish and Monitor:
- Publish the pipeline and monitor its execution to ensure successful data transfer.
Scenario 17 : Your organization needs to integrate data from multiple on-premises data sources into Azure Data Lake Storage for centralized analytics. How would you design this pipeline in ADF?
Answer: To integrate data from multiple on-premises sources into Azure Data Lake Storage using ADF:
- Self-hosted Integration Runtime (IR): Install and configure a Self-hosted IR on-premises to securely connect to on-premises data sources.
- Linked Services: Create linked services in ADF for each on-premises data source, associating them with the Self-hosted IR. Also, create a linked service for Azure Data Lake Storage.
- Datasets: Define datasets for each source and the destination, specifying the data structures involved.
- Pipeline Design: Design a pipeline that includes Copy Activities for each data source, transferring data to Azure Data Lake Storage. Use parallelism to handle multiple sources efficiently.
- Scheduling: Set up triggers to execute the pipeline at desired intervals, ensuring timely data integration.
Scenario 18: You need to implement a data pipeline that performs data validation before loading data into Azure SQL Database. How would you configure this in ADF?
Answer: To implement data validation before loading data into Azure SQL Database:
- Staging Area: First, load the raw data into a staging area, such as Azure Blob Storage or a staging table in Azure SQL Database.
- Data Validation: Use ADF’s Data Flow to perform validation checks on the data in the staging area. Implement transformations to filter out or correct invalid data.
- Conditional Logic: Incorporate an If Condition activity to determine if the data meets validation criteria. If valid, proceed to load into the destination table; if not, handle the errors accordingly.
- Error Handling: Log validation errors and notify stakeholders as necessary.
Scenario 19: Your pipeline must process data files that arrive at irregular intervals in Azure Blob Storage. How would you configure ADF to handle this scenario?
Answer: To process data files arriving at irregular intervals:
- Event-Based Trigger: Set up an Event-Based Trigger in ADF that listens for blob creation events in the specified storage container.
- Pipeline Configuration: Design a pipeline that processes the new file upon trigger activation.
- Concurrency Management: Configure the pipeline to handle multiple files arriving simultaneously by setting appropriate concurrency limits.
Scenario 20: You need to implement a pipeline that merges data from two different Azure SQL Databases into a single table in Azure Synapse Analytics. How would you approach this in ADF?
Answer: To merge data from two Azure SQL Databases into Azure Synapse Analytics:
- Linked Services: Create linked services for both Azure SQL Databases and Azure Synapse Analytics.
- Datasets: Define datasets for the source tables in both SQL Databases and the destination table in Synapse.
- Data Flow: Use ADF’s Mapping Data Flow to design a transformation that reads data from both sources, performs necessary joins or unions, and writes the merged data into the Synapse table.
- Pipeline Execution: Create a pipeline that executes the Data Flow activity.
Scenario 21: Your organization requires a pipeline that extracts data from a REST API and loads it into Azure Cosmos DB. How would you set this up in ADF?
Answer: To extract data from a REST API and load it into Azure Cosmos DB:
- Linked Services: Create a linked service for the REST API, providing the necessary endpoint and authentication details. Also, create a linked service for Azure Cosmos DB.
- Datasets: Define a dataset for the REST API response and another for the Cosmos DB container.
- Copy Activity: In a pipeline, add a Copy Activity that uses the REST API as the source and Cosmos DB as the sink. Configure mappings as needed.
- Pagination Handling: If the API returns paginated results, configure pagination rules in the Copy Activity to retrieve all data.
Scenario 22: You need to implement a pipeline that processes data differently based on the file type (e.g., CSV, JSON) in Azure Blob Storage. How would you design this in ADF?
Answer: To process data differently based on file type:
- Metadata Activity: Use a Get Metadata activity to retrieve the file name and type from the Blob Storage.
- Switch Activity: Incorporate a Switch activity to evaluate the file extension and branch the pipeline accordingly.
- Branching: Within each branch, define specific activities or Data Flows to process the file based on its type.
Scenario 23: Your pipeline must load data into a partitioned table in Azure SQL Data Warehouse. How would you configure this in ADF?
Answer: To load data into a partitioned table in Azure SQL Data Warehouse:
- Source Data Preparation: Ensure the source data includes the partition key column to facilitate correct data loading.
- Copy Activity: In the Copy Activity, map the source data to the destination table, ensuring the partition key is correctly assigned.
- Batching: Implement batching in the Copy Activity to load data in chunks, optimizing performance and managing resource utilization.
Scenario 24: You need to implement a pipeline that reads data from an SAP system and loads it into Azure Data Lake Storage. How would you set this up in ADF?
Answer: To read data from an SAP system and load it into Azure Data Lake Storage:
- Linked Service for SAP: Create a linked service in ADF that connects to the SAP system using the SAP Table connector, providing necessary connection details and credentials.
- Linked Service for Data Lake: Create a linked service for Azure Data Lake Storage.
- Datasets: Define datasets for the SAP source tables and
Scenario 25: Your organization requires a pipeline that extracts data from a REST API and loads it into Azure Cosmos DB. How would you set this up in ADF?
Answer: To extract data from a REST API and load it into Azure Cosmos DB:
- Linked Services: Create a linked service for the REST API, providing the necessary endpoint and authentication details. Also, create a linked service for Azure Cosmos DB.
- Datasets: Define a dataset for the REST API response and another for the Cosmos DB container.
- Copy Activity: In a pipeline, add a Copy Activity that uses the REST API as the source and Cosmos DB as the sink. Configure mappings as needed.
- Pagination Handling: If the API returns paginated results, configure pagination rules in the Copy Activity to retrieve all data.
Scenario 26: Your pipeline must load data into a partitioned table in Azure SQL Data Warehouse. How would you configure this in ADF?
Answer: To load data into a partitioned table in Azure SQL Data Warehouse:
- Source Data Preparation: Ensure the source data includes the partition key column to facilitate correct data loading.
- Copy Activity: In the Copy Activity, map the source data to the destination table, ensuring the partition key is correctly assigned.
- Batching: Implement batching in the Copy Activity to load data in chunks, optimizing performance and managing resource utilization.
Scenario 27: You need to implement a pipeline that reads data from an SAP system and loads it into Azure Data Lake Storage. How would you set this up in ADF?
Answer: To set up a pipeline in Azure Data Factory (ADF) that reads data from an SAP system and loads it into Azure Data Lake Storage, follow these steps:
Install Self-hosted Integration Runtime (IR):
- Install and configure IR on a machine with access to the SAP system.
Install SAP .NET Connector:
- Install the 64-bit SAP Connector for Microsoft .NET 3.0 on the IR machine.
Create Linked Services:
- For SAP: Use an appropriate SAP connector, provide SAP server details, and link the Self-hosted IR.
- For Azure Data Lake: Set up storage account access using keys or service principal.
Define Datasets:
- Source: Configure an SAP dataset with necessary extraction logic.
- Sink: Set up a dataset for Azure Data Lake, specifying file format and path.
Design Pipeline:
- Use a “Copy Data” activity to map SAP as the source and Azure Data Lake as the sink. Optimize queries and enable parallelism if needed.
Schedule and Monitor:
- Set triggers for automation and use monitoring tools for error handling and performance tracking.
Publish and Test:
- Publish the pipeline and test with sample data before full deployment.
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