Briefly Introduce Azure Data Factory (ADF) and its Importance in Data Integration and ETL Processes.

Azure Data Factory (ADF) is a cloud-based data integration service that helps you create and manage data pipelines. It's a fully managed service, so you don't have to worry about the underlying infrastructure. ADF can be used to connect to a wide variety of data sources, including relational databases, NoSQL databases, cloud storage, and SaaS applications. Once you've connected to your data sources, you can use ADF to create data pipelines that transform and move your data. ADF pipelines are code-free, so you don't need to write any code to get started.

ADF is an important tool for data integration and ETL processes because it provides a central platform for managing all of your data pipelines. ADF can help you to:

• Consolidate data from multiple sources into a single, unified view.
• Transform data to meet your specific business needs.
• Move data to the cloud or on-premises systems.
• Automate your data integration processes.

ADF is a powerful tool that can help you improve the efficiency and accuracy of your data integration and ETL processes. It's a valuable tool for any organization that wants to get the most out of its data.

Explain Why ADF Interview Questions Are Critical For Candidates Aiming For Data Engineering or Cloud Roles.

Azure Data Factory (ADF) interview questions are critical for candidates aiming for data engineering or cloud roles because they assess the candidate's knowledge of a key tool used in these fields. ADF is a cloud-based data integration service that helps organizations connect to, transform, and move data between different data sources. It is a powerful tool that can be used to build complex data pipelines that automate the movement and transformation of data.

Candidates who are familiar with ADF will be able to demonstrate their understanding of data integration and ETL processes. They will also be able to show that they have the skills necessary to design and implement data pipelines. This knowledge is essential for data engineers and cloud architects, who are responsible for designing and managing data systems.

In addition, ADF interview questions can help to assess the candidate's problem-solving skills and their ability to think critically. Candidates who can answer ADF interview questions effectively will be able to show that they have the skills and knowledge necessary to be successful in data engineering or cloud roles.

Common Azure Data Factory Interview Questions

Common Azure Data Factory (ADF) interview questions include:

• What is Azure Data Factory (ADF)?
• What are the benefits of using ADF?
• What are the different types of data sources that ADF can connect to?
• What are the different types of data transformations that ADF can perform?
• How can ADF be used to automate data integration processes?
• What are the best practices for designing and implementing ADF pipelines?
• What are the different monitoring and troubleshooting tools available for ADF?
• What are the latest features and updates to ADF?

In addition to these technical questions, interviewers may also ask about your experience with ADF and your understanding of data integration and ETL processes. They may also ask about your problem-solving skills and your ability to work in a team.

To prepare for your ADF interview, it is important to have a strong understanding of the concepts and features of ADF. You should also be able to demonstrate your experience with ADF and your ability to apply it to real-world data integration scenarios.

Basic Questions:

Basic Azure Data Factory (ADF) interview questions assess your fundamental knowledge of ADF and its capabilities. These questions may include:

• What is Azure Data Factory (ADF)?
• What are the benefits of using ADF?
• What are the different types of data sources that ADF can connect to?
• What are the different types of data transformations that ADF can perform?
• How can ADF be used to automate data integration processes?

To answer these questions effectively, you should have a clear understanding of the core concepts and features of ADF. You should also be able to articulate the benefits of using ADF for data integration and ETL processes.

Here are some tips for answering basic ADF interview questions:

• Be clear and concise in your answers.
• Use specific examples to illustrate your points.
• Demonstrate your understanding of ADF's capabilities and how it can be used to solve real-world data integration problems.

By preparing for and answering basic ADF interview questions effectively, you can show the interviewer that you have a solid foundation in ADF and its capabilities.

What is Azure Data Factory, and How Does It Work?

Azure Data Factory (ADF) is a cloud-based data integration service that helps you to create, schedule, and manage data pipelines. It is a fully managed service, so you don't have to worry about the underlying infrastructure. ADF can be used to connect to a wide variety of data sources, including relational databases, NoSQL databases, cloud storage, and SaaS applications. Once you've connected to your data sources, you can use ADF to create data pipelines that transform and move your data.

ADF pipelines are code-free, so you don't need to write any code to get started. You can simply use the ADF user interface to drag and drop activities into your pipeline. ADF provides a variety of activities that you can use to perform data transformations, such as filtering, sorting, joining, and aggregating data. You can also use ADF to move data between different data sources.

Once you've created your data pipeline, you can schedule it to run on a regular basis. ADF will automatically monitor your pipeline and ensure that it runs successfully. You can also use ADF to monitor the performance of your data pipeline and troubleshoot any issues that may occur.

ADF is a powerful tool that can help you improve the efficiency and accuracy of your data integration and ETL processes. It is a valuable tool for any organization that wants to get the most out of its data.

What are the Key Components of ADF (e.g., Pipelines, Activities, Datasets, Linked Services)?

The key components of Azure Data Factory (ADF) are:

• Pipelines: Pipelines are the core component of ADF. They define the data flow and transformations that will be performed on your data. Pipelines are made up of activities and datasets.
• Activities: Activities are the individual steps that are performed in a pipeline. ADF provides a wide range of activities that you can use to perform data transformations, such as filtering, sorting, joining, and aggregating data. You can also use ADF to move data between different data sources.
• Datasets: Datasets represent the data that is used in a pipeline. Datasets can be created from a variety of data sources, such as relational databases, NoSQL databases, cloud storage, and SaaS applications.
• Linked services: Linked services are used to connect ADF to your data sources and sinks. Linked services provide the credentials and connection information that ADF needs to access your data.

These four components work together to create a data integration solution that is tailored to your specific needs. ADF is a powerful tool that can help you improve the efficiency and accuracy of your data integration and ETL processes.

Intermediate Questions:

Intermediate Azure Data Factory (ADF) interview questions assess your understanding of ADF's more advanced features and capabilities. These questions may include:

• How can ADF be used to orchestrate complex data pipelines?
• What are the different types of data transformations that can be performed in ADF?
• How can ADF be used to load data into and out of Azure Synapse Analytics?
• What are the different ways to monitor and troubleshoot ADF pipelines?
• How can ADF be used to implement data governance and security best practices?

To answer these questions effectively, you should have a good understanding of ADF's architecture and its capabilities. You should also be able to demonstrate your experience with ADF and your ability to apply it to real-world data integration scenarios.

Here are some tips for answering intermediate ADF interview questions:

• Be clear and concise in your answers.
• Use specific examples to illustrate your points.
• Demonstrate your understanding of ADF's capabilities and how it can be used to solve complex data integration problems.

By preparing for and answering intermediate ADF interview questions effectively, you can show the interviewer that you have a solid understanding of ADF and its capabilities.

How Do You Handle Error Handling and Retry Mechanisms in ADF?

Error handling and retry mechanisms are essential for ensuring the reliability and robustness of your Azure Data Factory (ADF) pipelines. ADF provides several features that can help you to handle errors and retries, including:

• Activity retries: ADF allows you to specify the number of times that an activity will be retried if it fails. You can also specify the interval between retries.
• Pipeline retries: ADF allows you to specify the number of times that a pipeline will be retried if it fails. You can also specify the interval between retries.
• Error handling activities: ADF provides several activities that can be used to handle errors. For example, you can use the Web activity to send an email notification if an activity fails.

In addition to these ADF-specific features, you can also use Azure Functions to handle errors and retries in your pipelines. Azure Functions are serverless functions that can be triggered by ADF events. You can use Azure Functions to perform a variety of tasks, such as sending email notifications, logging errors, and retrying failed activities.

By using a combination of ADF features and Azure Functions, you can create robust and reliable data pipelines that can handle errors and retries gracefully.

What is the Difference Between Mapping Data Flows and SSIS in ADF?

Mapping data flows and SSIS (SQL Server Integration Services) are both data transformation technologies that can be used in Azure Data Factory (ADF). However, there are some key differences between the two technologies:

• Development environment: Mapping data flows are developed in the ADF user interface, while SSIS packages are developed in Visual Studio.
• Code-free vs. code-based: Mapping data flows are code-free, while SSIS packages are code-based.
• Data sources: Mapping data flows can connect to a wider variety of data sources than SSIS, including cloud-based data sources.
• Transformations: Mapping data flows provides a wider range of data transformations than SSIS, including machine learning transformations.
• Scalability: Mapping data flows are more scalable than SSIS packages, as they can be run on a distributed cluster.

In general, mapping data flows are a better choice for developing complex data transformations that require high scalability. SSIS packages are a better choice for developing data transformations that require custom code or that need to be integrated with other SSIS components.

Here is a table that summarizes the key differences between mapping data flows and SSIS:

Advanced Questions:

Advanced Azure Data Factory (ADF) interview questions Assess your knowledge of ADF's most advanced features and capabilities. These questions may include:

• How can ADF be used to implement data governance and security best practices?
• What are the different ways to optimize the performance of ADF pipelines?
• How can ADF be used to integrate with other Azure services, such as Azure Machine Learning and Azure Synapse Analytics?
• What are the latest features and updates to ADF?

To answer these questions effectively, you should have a deep understanding of ADF's architecture and its capabilities. You should also be able to demonstrate your experience with ADF and your ability to apply it to complex data integration scenarios.

Here are some tips for answering advanced ADF interview questions:

• Be clear and concise in your answers.
• Use specific examples to illustrate your points.
• Demonstrate your understanding of ADF's capabilities and how it can be used to solve complex data integration problems.

By preparing for and answering advanced ADF interview questions effectively, you can show the interviewer that you have a deep understanding of ADF and its capabilities.

How Do You Optimize Pipeline Performance in ADF?

There are several ways to optimize pipeline performance in Azure Data Factory (ADF). Here are a few tips:

• Use the correct data source and sink connectors: ADF provides a variety of data sources and sink connectors. Choose the connector that is most efficient for your data source and sink. For example, if you are loading data from a relational database, use the ADF relational database connector instead of the generic ODBC connector.
• Partition your data: If your data is large, partition it into smaller chunks. This will improve the performance of your pipeline, as ADF can process each partition in parallel.
• Use the correct activity types: ADF provides a variety of activity types, such as the Copy Activity, the Data Flow Activity, and the HDInsight Activity. Choose the activity type that is most efficient for your task. For example, if you are simply copying data from one data source to another, use the Copy Activity. If you need to perform more complex data transformations, use the Data Flow Activity or the HDInsight Activity.
• Optimize your data transformations: If your pipeline performs complex data transformations, optimize your transformations to improve performance. For example, avoid using nested loops and use efficient data structures.
• Monitor your pipeline performance: ADF provides a variety of monitoring tools that you can use to track the performance of your pipelines. Use these tools to identify any bottlenecks in your pipeline and make adjustments to improve performance.

By following these tips, you can optimize the performance of your ADF pipelines and ensure that they run efficiently.

Can You Explain How To Use Parameters and Variables in ADF?

Parameters and variables are two powerful features in Azure Data Factory (ADF) that can be used to make your pipelines more dynamic and reusable.

Parameters are used to pass values into your pipeline when it is executed. This can be useful for passing in different values for different runs of the pipeline, or for passing in values from external sources.

Variables are used to store values within your pipeline. This can be useful for storing intermediate values or for passing values between different activities in your pipeline.

To use parameters in your ADF pipeline, you can use the @ symbol followed by the name of the parameter. For example, if you have a parameter named "source_table", you can use it in your pipeline like this:

copy_activity = CopyActivity(
    name="CopyActivity",
    source=source_dataset,
    sink=sink_dataset,
    source_table="@source_table"
)

To use variables in your ADF pipeline, you can use the @{} syntax. For example, if you have a variable named "source_table", you can use it in your pipeline like this:

source_table = Variable(
    name="source_table",
    value="my_source_table"
)

Parameters and variables are a powerful way to make your ADF pipelines more dynamic and reusable. By using parameters and variables, you can easily pass in different values for different runs of your pipeline, or store intermediate values for use in later activities.

Scenario-Based Questions

Scenario-based Azure Data Factory (ADF) interview questions assess your ability to apply your knowledge of ADF to real-world data integration scenarios. These questions may include:

• You are tasked with designing an ADF pipeline to load data from a SQL Server database into an Azure Synapse Analytics table. The data needs to be transformed and cleansed before it is loaded into the Azure Synapse Analytics table. How would you design and implement this pipeline?

• You are working on a project to migrate data from an on-premises data warehouse to Azure Data Lake Storage. The data is in a variety of formats, including CSV, Parquet, and JSON. How would you design and implement an ADF pipeline to migrate this data?

• You are tasked with creating an ADF pipeline to orchestrate a complex data processing workflow. The workflow involves multiple activities, including data extraction, transformation, and loading. How would you design and implement this pipeline to ensure that it is efficient and reliable?

To answer these questions effectively, you should demonstrate your understanding of ADF's capabilities and your ability to apply it to solve real-world data integration problems. You should also be able to articulate your design decisions and explain how you would implement your pipeline.

Here are some tips for answering scenario-based ADF interview questions:

• Be clear and concise in your answers.
• Use specific examples to illustrate your points.
• Demonstrate your understanding of ADF's capabilities and how it can be used to solve complex data integration problems.
• Articulate your design decisions and explain how you would implement your pipeline.

By preparing for and answering scenario-based ADF interview questions effectively, you can show the interviewer that you have the skills and knowledge necessary to be successful in an ADF role.

How Would You Design an ETL Pipeline To Process Data From Multiple Sources?

To design an ETL pipeline to process data from multiple sources in Azure Data Factory (ADF), you would need to follow these steps:

1. Identify the data sources: Determine the different data sources that you need to connect to, and the type of data that each source contains.
2. Create linked services: Create linked services in ADF to connect to each of the data sources. Linked services provide the credentials and connection information that ADF needs to access your data.
3. Create datasets: Create datasets in ADF to represent the data that you want to process from each of the data sources. Datasets provide a structured view of your data, and they can be used to define the schema of the data that you want to process.
4. Create a data flow activity: Create a data flow activity in ADF to define the data transformations that you want to perform on the data. Data flow activities provide a visual interface for creating data transformations, and they can be used to perform a variety of transformations, such as filtering, sorting, joining, and aggregating data.
5. Create a pipeline: Create a pipeline in ADF to orchestrate the execution of the data flow activity. Pipelines define the sequence of activities that you want to perform, and they can be used to schedule the execution of the pipeline and monitor its progress.

Once you have created the ETL pipeline, you can schedule it to run on a regular basis. ADF will automatically monitor the pipeline and ensure that it runs successfully. You can also use ADF to monitor the performance of the pipeline and troubleshoot any issues that may occur.

What Steps Would You Take To Migrate An On-Premises ETL Process To Azure Data Factory?

To migrate an on-premises ETL process to Azure Data Factory (ADF), you would need to follow these steps:

1. Assess the existing ETL process: Determine the current ETL process, including the data sources, transformations, and target systems. Identify any dependencies or limitations of the existing process.
2. Design the ADF pipeline: Create an ADF pipeline that replicates the functionality of the existing ETL process. Use ADF's drag-and-drop interface to create activities and datasets, and configure the data transformations and data flow.
3. Connect to data sources: Create linked services in ADF to connect to the source and target systems used in the ETL process. Ensure that ADF has the necessary permissions and credentials to access the data.
4. Migrate data: Use ADF's copy activity to migrate the data from the on-premises systems to Azure. Configure the copy activity to handle any data type conversions or schema changes.
5. Test and validate: Thoroughly test the ADF pipeline to ensure that it is functioning as expected. Validate the data quality and accuracy of the migrated data.
6. Deploy and monitor: Deploy the ADF pipeline to the Azure cloud and schedule it to run regularly. Use ADF's monitoring and alerting features to track the performance and health of the pipeline.

By following these steps, you can successfully migrate an on-premises ETL process to Azure Data Factory. ADF provides a scalable, reliable, and cost-effective solution for managing and automating data integration and ETL processes in the cloud.

Tips To Prepare for ADF Interviews

To prepare for Azure Data Factory (ADF) interviews, consider the following tips:

• Understand the basics of ADF: Familiarize yourself with the key concepts and features of ADF, such as pipelines, activities, datasets, and linked services. Understand how ADF can be used for data integration and ETL processes.
• Practice creating and managing ADF pipelines: Build sample pipelines using ADF's user interface or code-based approaches. Experiment with different data sources, transformations, and activities to gain hands-on experience.
• Learn about ADF's advanced features: Explore ADF's capabilities for data governance, security, optimization, and monitoring. Understand how to use these features to enhance the reliability, performance, and security of your data pipelines.
• Prepare for common interview questions: Review common ADF interview questions, including those related to basic concepts, intermediate topics, and advanced scenarios. Prepare clear and concise answers that demonstrate your understanding and experience with ADF.
• Showcase your problem-solving skills: During the interview, be prepared to discuss real-world data integration challenges and how you would approach them using ADF. Explain your thought process and the technical solutions you would implement.
• Highlight your experience and projects: Quantify your experience with ADF or similar data integration tools. Discuss projects where you successfully implemented ADF solutions and the impact they had on your organization.

By following these tips and preparing thoroughly, you can increase your chances of success in ADF interviews and demonstrate your proficiency in this essential data integration technology.

Understand Core Concepts Like Triggers, Integration Runtime, and Data Movement.

Understanding core concepts in Azure Data Factory (ADF) is crucial for successful data integration and ETL processes. Here are three key concepts to grasp:

• Triggers: Triggers define the conditions under which an ADF pipeline will start running. You can configure triggers based on schedules, events, or manual execution. Understanding triggers allows you to automate and orchestrate your data pipelines effectively.
• Integration runtime: Integration runtime is the compute infrastructure that ADF uses to execute pipelines. There are different types of integration runtimes, such as Azure IR, Self-Hosted IR, and AutoResolve IR. Choosing the appropriate integration runtime is essential for optimizing the performance and cost of your pipelines.
• Data movement: Data movement is a fundamental aspect of ADF pipelines. ADF provides various activities for moving data between different data sources and sinks. Understanding the capabilities and limitations of these activities, such as the Copy Activity, Data Flow Activity, and HDInsight Activity, is crucial for designing efficient and reliable data pipelines.

By having a solid grasp of these core concepts, you can effectively design, implement, and manage ADF pipelines that meet your business requirements. This understanding will also be valuable during Azure Data Factory interview questions, as interviewers often assess candidates' proficiency in these fundamental areas.

Practice Building Pipelines and Working With ADF in the Azure Portal.

Hands-on experience is invaluable when it comes to preparing for Azure Data Factory (ADF) interviews. To enhance your practical skills, consider the following tips:

• Build pipelines in the Azure portal: The ADF user interface in the Azure portal provides a drag-and-drop canvas for designing and creating pipelines. Experiment with different data sources, transformations, and activities to gain a practical understanding of how ADF pipelines work.
• Work with ADF in code: While the Azure portal offers a user-friendly interface, it's also beneficial to work with ADF using code. Explore the ADF .NET or Python SDKs to create and manage pipelines programmatically. This will give you a deeper understanding of the underlying ADF architecture.
• Utilize ADF samples and tutorials: Microsoft provides a range of samples and tutorials for ADF. These resources offer practical guidance on how to perform common data integration tasks using ADF. Working through these examples will help you build your skills and prepare for interview questions that require coding or problem-solving.

By practicing building pipelines and working with ADF in the Azure portal, you can demonstrate your proficiency in the practical aspects of ADF during interviews. This hands-on experience will also boost your confidence and enable you to answer technical questions with greater clarity and precision.

Familiarize Yourself With Real-World Use Cases And Troubleshooting Techniques.

To enhance your preparation for Azure Data Factory (ADF) interviews, consider the following strategies:

• Explore real-world use cases: Familiarize yourself with how organizations are using ADF to solve real-world data integration challenges. Read case studies, attend webinars, and engage with the ADF community to gain insights into practical applications of the technology.
• Practice troubleshooting techniques: Troubleshooting is an essential skill for ADF engineers. Study common ADF errors and their potential solutions. Experiment with different scenarios in your own ADF environment to develop a hands-on understanding of how to diagnose and resolve issues.
• Utilize ADF documentation and resources: Microsoft provides comprehensive documentation and resources for ADF. Thoroughly review the documentation to gain a deep understanding of ADF's capabilities and best practices. Additionally, explore online forums and communities where you can engage with other ADF users and experts.

By familiarizing yourself with real-world use cases and troubleshooting techniques, you can demonstrate to interviewers that you have a practical understanding of how ADF is applied in various scenarios.

This knowledge will also enable you to confidently address questions related to problem-solving and troubleshooting, which are commonly asked in ADF interviews.

Conclusion

Preparing for Azure Data Factory (ADF) interviews requires a multifaceted approach. By understanding the core concepts, practicing pipeline building, familiarizing yourself with real-world use cases, and honing your troubleshooting skills, you can increase your chances of success.

Remember to tailor your preparation to the specific role and organization you are applying to. Research the company's data integration needs and the specific ADF skills they are seeking. With thorough preparation and a solid understanding of ADF's capabilities, you can confidently navigate the interview process and showcase your proficiency in this powerful data integration technology.

Best of luck in your Azure Data Factory interview preparation and future endeavors.

Summarize the Importance Of Mastering ADF Concepts For Interviews.

Mastering Azure Data Factory (ADF) concepts is crucial for success in ADF interviews. Interviewers seek candidates with a deep understanding of the technology's core components, capabilities, and best practices. By demonstrating proficiency in ADF concepts, you can:

• Convey a strong foundation: A solid understanding of ADF concepts indicates that you have a comprehensive grasp of the technology's architecture, functionality, and potential.
• Articulate your knowledge: Clearly explaining ADF concepts during an interview showcases your ability to communicate technical information effectively.
• Solve problems efficiently: A thorough understanding of ADF concepts enables you to analyze and solve data integration challenges during the interview process.
• Discuss real-world applications: Interviewers often ask about how ADF can be applied to solve specific business problems. By mastering ADF concepts, you can provide informed answers and demonstrate your understanding of practical use cases.

Investing time in mastering ADF concepts is essential for showcasing your expertise and increasing your chances of success in ADF interviews. It not only demonstrates your technical proficiency but also highlights your ability to apply your knowledge to real-world scenarios.

Encourage Readers To Practice and Explore Azure Documentation For Deeper Understanding.

Beyond the interview preparation tips outlined above, it is highly recommended to engage in hands-on practice and delve into Azure documentation for a deeper understanding of Azure Data Factory (ADF).

Practice Regularly: The best way to solidify your understanding of ADF concepts is through regular practice. Create sample pipelines, experiment with different data sources and transformations, and troubleshoot common issues. This hands-on experience will not only enhance your technical skills but also build your confidence in using ADF.

Explore Azure Documentation: Microsoft provides comprehensive documentation for ADF, covering everything from basic concepts to advanced features. Take advantage of this resource to expand your knowledge, learn about best practices, and stay up-to-date with the latest updates. Thoroughly reading and understanding the documentation will demonstrate your commitment to continuous learning and your eagerness to master ADF.

By dedicating time to practice and exploring Azure documentation, you will not only prepare effectively for ADF interviews but also lay a strong foundation for your future success as an ADF engineer.

Azure Data Factory Interview Questions 

Get Latest 2025 Updated Questions and Answers: https://dumpsarena.com/vendor/microsoft/

1. What is Azure Data Factory primarily used for?

A) Real-time data processing 

B) Data visualization 

C) ETL (Extract, Transform, Load) and data integration 

D) Machine learning model training 

2. Which of the following is NOT a component of Azure Data Factory?

A) Pipeline 

B) Dataset 

C) Data Flow 

D) Data Warehouse 

3. What is a Pipeline in Azure Data Factory?

A) A storage unit for raw data 

B) A logical grouping of activities to perform a task 

C) A data transformation tool 

D) A visualization tool for data 

4. Which activity is used to execute a stored procedure in Azure Data Factory?

A) Copy Activity 

B) Lookup Activity 

C) Stored Procedure Activity 

D) Data Flow Activity 

5. What is the purpose of the Copy Activity in Azure Data Factory?

A) To transform data 

B) To move data between source and sink 

C) To execute SQL queries 

D) To create data visualizations 

6. Which of the following is a supported source in Azure Data Factory?

A) Azure Blob Storage 

B) Amazon S3 

C) Google BigQuery 

D) All of the above 

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

A) A connection to an external data source 

B) A data transformation tool 

C) A visualization tool 

D) A data storage unit 

8. Which of the following is NOT a type of trigger in Azure Data Factory?

A) Schedule Trigger 

B) Event Trigger 

C) Tumbling Window Trigger 

D) Manual Trigger 

9. What is the purpose of a Data Flow in Azure Data Factory?

A) To copy data between sources 

B) To transform data at scale 

C) To execute stored procedures 

D) To visualize data 

10. Which of the following is true about Mapping Data Flows?

A) They require coding in Python 

B) They are executed on Spark clusters 

C) They are used for real-time data processing 

D) They cannot be used with Azure SQL Database 

11. What is the purpose of the Lookup Activity in Azure Data Factory?

A) To copy data from one source to another 

B) To retrieve a dataset or value for use in subsequent activities 

C) To transform data 

D) To execute a stored procedure 

12. Which of the following is a valid sink in Azure Data Factory?

A) Azure SQL Database 

B) Azure Data Lake Storage 

C) Azure Cosmos DB 

D) All of the above 

13. What is the purpose of Integration Runtime in Azure Data Factory?

A) To provide compute infrastructure for data movement and transformation 

B) To visualize data 

C) To store data 

D) To execute machine learning models 

14. Which Integration Runtime is used for data movement between on-premises and cloud?

A) Azure Integration Runtime 

B) Self-hosted Integration Runtime 

C) SSIS Integration Runtime 

D) None of the above 

15. What is the maximum number of activities allowed in a single pipeline?

A) 10 

B) 40 

C) 100 

D) Unlimited 

16. Which of the following is NOT a supported data transformation in Mapping Data Flows?

A) Aggregate 

B) Join 

C) Pivot 

D) Machine Learning 

17. What is the purpose of the Tumbling Window Trigger?

A) To trigger pipelines at fixed intervals 

B) To trigger pipelines based on events 

C) To trigger pipelines manually 

D) To trigger pipelines based on data availability 

18. Which of the following is true about Azure Data Factory's pricing model?

A) It is based on the number of pipelines created 

B) It is based on the number of activities executed 

C) It is based on the volume of data processed 

D) It is based on the number of users 

19. What is the purpose of the Get Metadata Activity?

A) To retrieve metadata about data in a dataset 

B) To copy data between sources 

C) To transform data 

D) To execute a stored procedure 

20. Which of the following is NOT a supported file format in Azure Data Factory?

A) JSON 

B) CSV 

C) XML 

D) MP4 

21. What is the purpose of the Web Activity in Azure Data Factory?

A) To call a REST API  

B) To copy data between sources 

C) To transform data 

D) To execute a stored procedure 

22. Which of the following is true about Azure Data Factory's monitoring capabilities?

A) It provides real-time monitoring of data pipelines 

B) It allows monitoring through Azure Monitor and Log Analytics 

C) It does not support logging 

D) It only provides basic metrics 

23. What is the purpose of the If Condition Activity in Azure Data Factory?

A) To copy data between sources 

B) To execute conditional logic in a pipeline 

C) To transform data 

D) To execute a stored procedure 

24. Which of the following is true about Azure Data Factory's security features?

A) It supports Azure Active Directory integration 

B) It does not support encryption 

C) It does not support role-based access control (RBAC) 

D) It only supports on-premises data sources 

25. What is the purpose of the ForEach Activity in Azure Data Factory?

A) To iterate over a collection of items 

B) To copy data between sources 

C) To transform data 

D) To execute a stored procedure