Azure Data Factory’s Interview Blog helps you find jobs requiring expertise.

Interview questions on Azure Data Factory often crop up when looking for jobs with Azure Data Factory.

These Azure data factory interview concerns will be covered here, along with technology fundamentals and building and maintaining data pipelines.

Thus, Azure Data Factory interview questions give you all the knowledge you need for an effortless Azure Data Factory interview experience!

Azure Data Factory, a cloud data integration service, facilitates easy creation, management, and monitoring of pipelines that process and transform data from multiple sources.

However, to use it effectively, one must possess in-depth knowledge of its technology and fundamental concepts to operate with Azure Data Factory effectively.

In this article, I will present some typical interview questions thatAzure data factorycandidates might face when interviewing for jobs that use this solution.

Practice answering these beforehand to impress your hiring manager with your knowledge and abilities!

When interviewing for positions that require working with Azure Data Factory, be ready for questions designed to assess your knowledge and skills.

This blog’s Azure Data Factory real-time interview questions post provides an essential overview of common Azure Data Factory interview questions and answers aimed at getting through.

Everything will be covered here, from grasping fundamentals to advanced capabilities and best practices!

1. What is Azure?

Azure is a cloud computing platform and online portal provided by Microsoft that offers various services for cloud computing. It allows users to access and manage resources and services without incurring infrastructure costs.

2. Can Azure build and manage applications on a massive global network?

Azure can build, manage, and deploy applications on a massive global network using preferred tools and frameworks. Users can create app services, web applications, and other resources on the platform.

3. In what ways has Azure improved GitHub?

Azure’s significant contribution to GitHub is becoming the only cloud-weight integrated support for Red Hat. This allows users to reach Red Hat directly for Linux-related issues, reducing the need for multiple channels and increasing the speed at which problems are resolved.

4. Have you heard of Azure Data Factory?

Azure Data Factory is a service that allows users to connect to any relational or non-relational source and perform transformations on the data without creating a third layer. It provides link services for connections to different data stores, fetching data, performing transformations, and loading it.

5. Describe Azure Data Breaks.

Azure Data Breaks are used for ETL tasks but allow code writing.

6. Tell me about Azure SQL DB.

Azure SQL DB is a fully managed, scalable, and secure database service that runs SQL Server in the cloud.

7. Do you know what Azure Synapse Analytics is?

It is a fully managed, end-to-end data analytics service that integrates data warehousing, machine learning, and data integration capabilities in a single service.

8. What is Azure Elastic Pool?

Azure Elastic Pool is a fully managed, scalable, and high-performance relational database service that allows users to create and manage database pools of identical, fully managed SQL Database instances.

9. Would you tell me about Azure Cosmos DB?

Azure Cosmos DB is a globally distributed, multi-model database service with high availability and low latency.

10. How can I understand ADLS or Azure Data Lake Storage?

Azure Data Lake Storage (ADLS) combines ADLS and block storage, providing non-relational storage for relational data.

11. So, what exactly are Azure SQL Warehouses?

Azure SQL Warehouses are relational DBs for search and abstract data.

12. Tell me about Azure Stream Analytics.

Azure Stream Analytics is a serverless, scalable processing engine by Microsoft that allows real-time analytics on multiple streams from various sources, such as devices, sensors, websites, and social media.

13. What is Azure HBase?

Azure HBase is a non-relational database used to query big data. It is a column data store, and HD Inside is an application that helps connect to big data. The data is denormalised, and the schema is defined on the right. When writing data, the schema depends on the source, and consistency across concurrent transactions is guaranteed at the column family level.

14. To what extent does Azure Cosmos DB use networking?

Networking options include public endpoints or selected networks. Users can configure firewalls and automatically allow access to them. Users can now create a virtual network with all networks and tags.

15. Could you tell me the different storage options that Azure Cosmos DB offers?

There are two options available: standard and premium storage accounts. Magnetic drives back traditional storage accounts and provides the lowest cost per G, while solid-state drives back premium storage accounts and offers consistently low latency performance.

16. Which replication options does Azure Cosmos DB provide?

Replication options include locally redundant storage, geo read access, and geo zone redundant storage. Locally redundant storage is located in the same place where the D B resides, while geo read access is geographically different regions.

17. What kinds of access styles exist when using Azure Cosmos DB?

Access styles are available, with hot access ideal for frequently accessed data and incredible access for infrequently accessed data. The data temperature affects the charging process, with storage being cheaper but data access becoming expensive.

18. If users want to keep Azure Cosmos DB secure, what steps can they take?

Users can create and add users, which requires them to be in the same subscription. This is known as rollback-based access control. Users can also enable multi-factor authentication. However, access at the subscription level does not guarantee access to Cosmos DB.

19. Tell me the three parts of Azure Data Factory’s data pipeline flow.

The input dataset, pipeline, and output dataset. The input dataset is the source data captured from the source, while the pipeline performs operations on the data to transform it. Output dataset contains structured data that has been changed in the pipeline, which can be stored in various locations such as data lake stores, block stores, or SQL.

20. Can you tell me what Azure Data Factory’s link services are?

Link services, information stored in Azure Data Battery, are essential for connecting to external data sources.

21. To what does Azure Data Factory refer to as a gateway?

A gateway, also known as a data gateway or integration runtime, connects on-premises data to the cloud without requiring a client to be installed on the on-premises system.

22. Which of Azure Data Factory’s services does it offer for rapid data integration?

Azure Data Factory provides a single high-speed data integration service for all scale levels.

23. Azure Data Factory offers three kinds of integration runtimes; which ones are you familiar with?

Azure Auto-Resolve, self-hosted, and Azure SSIS.

24. What is Azure Virtual Networks?

Azure Virtual Networks connect to on-premises systems and the cloud, and Apache Spark programs can be run using the integration runtime.

25. How does Azure Data Factory’s integration runtime work?

The integration runtime is used when the source and destination are cloud-based, while self-hosted is used for on-premises components. Self-hosted integration runtimes are exclusive to running SSIS packages.

26. Does Azure Data Lake Storage have a price?

The cost of the storage account is not only related to storage costs but also includes read and write costs.

27. Can you tell me about the data lake design?

The data lake architecture is a system that stores and processes data from upstream sources, such as a database, to downstream subscribers.

28. Why does the data lake design call for publishing labs?

Publish labs, such as dimension modelling, are used for downstream scan reports.

29. When it comes to data lake design, what exactly is staging for?

Data is moved from staging to an intermediate school layer, where quality checks are performed.

30. How does the data lake design use the school layer?

The goal is then transferred to publish, where transformations are completed, and the data is moved to the main tables.

31. Could you please explain how Azure Data Lake works?

Azure Data Lake is a platform that allows developers, data scientists, and analysts to store data in various sizes and shapes. It includes file system storage, object storage, and analytics support.

32. Compare Azure Data Lake Generation 1 with Azure Data Lake Generation 2. How are they different?

Azure Data Lake Generation 1 uses file system storage for distributed data and blocks in high-rate file systems. In contrast, Azure Data Lake Generation 2 includes both file system storage for performance and security and object storage for scalability.

33. In what ways is pipeline scheduling made possible by the Azure data factory?

The Azure data factory allows scheduling pipelines on a specific basis, such as weekly, monthly, or data-based.

34. With Azure SQL Server, how can one set up a SQL server for a particular project?

Users can import the primary data from the channels into the SQL database to create an SQL server for a specific project. The server will be made using SQL Server Management, which allows users to connect to the instance using Azure in parallel.

35. Why would one want to bring an Azure SQL database in or out?

To import or export an Azure SQL database without allowing Azure services to access the server, create an Azure virtual machine and install the SQL package. Create a firewall rule to enable V M access to the database. Export the database using the SQL package.

36. How does one benefit from transferring information from Azure Data Lake to Azure SQL Server?

After creating the database on the Azure SQL server, import the data from all tables to the server. Edit the mappings and change the data type using Inquiry or manual methods.

37. Why would one want to establish an Azure data lake?

To format the database, create a data lake in Azure by going to storage accounts and adding a show account. This will be your Azure data lake.

38. In Azure, what does the data factory serve to accomplish?

The Azure data factory is a powerful tool for creating pipelines and data extraction processes. It allows users to build pipelines manually or visually. Users can choose processing services or insert custom code as processing steps in pipelines.

39. Tell me about the Azure data factory’s Git repository and its function.

The Azure data factory provides a single hybrid data integration service for all skill levels, allowing users to build pipelines manually or visually. Users can choose processing services or insert custom code as processing steps in pipelines.

40. How can access control be added to the Azure data factory?

Access control can be added to the data factor by providing access control for someone, a service principal, or a group.

41. When using Azure Data Factory, how does one create a data set?

To publish the data sets, click “publish all” periodically. If creating another data set, clone the existing data set by clicking “all.” However, this forceful refresh may cause the other flow on the data set to become unavailable.

42. Explain the function of the link service in Azure Data Factory.

The Azure Data Factory provides a link service that connects a source and a destination system.

43. In Azure Data Factory, how does one move files from one location to another?

When passing another file, the time in the target changes, and the process continues to progress. The goal is to make the pipeline more dynamic by dynamically deciding the file name during runtime.

44. What is the “Azure data bricks” purpose in Azure Data Lake?

Azure Data Bricks is a processing engine that provides a high-performance, in-memory cluster for data processing. It supports batch and real-time processing and allows for parallel read and write operations.

45. Which objective tier does Azure Data Bricks support?

The Azure Data Bricks goal tier is a processing engine that provides a high-performance, in-memory cluster for data processing. It supports batch and real-time processing and allows for parallel read and write operations.

46. Tell me what published data Azure data bricks are.

The Azure data bricks published data is a processed data set transformed from the goal to publish.

47. For what purposes may one utilise Azure Data Factory with Azure Data Bricks?

The Azure Data Factory uses Azure Data Bricks as a processing service to execute the data extra.

“Azure Data Factoryscenario-based interview questionsoften contain multiple-choice questions.

These questions test your knowledge of Azure Data Factory features and concepts by selecting the proper answer from a list.

When answering Azure Data Factory multiple-choice questions, read each question carefully and consider all options before choosing. Crossing out the apparent choices may help you decide.

You may demonstrate your expertise by practising Azure Data Factory multiple-choice questions (MCQs) before interviews.”

1. Which of the following is not a feature of Azure’s scalability?

a) With a few clicks, you can raise or decrease the size of the database.

b) Capacity to utilise various cloud service providers

c) Proficiency in open-source software

d) Restrictions on using just one cloud service provider

Answer: b) Capacity to utilise various cloud service providers

2. What is no benefit of using Azure for cloud enterprise and hybrid infrastructure?

a) Increased flexibility

b) Improved security

c) Reduced scalability

d) Lower pricing

Answer: c) Reduced scalability

3. Which of the following is not a type of non-relational database?

a) Document data store

b) Column data store

c) Key-value data stored

d) Time series data store

Answer: d) Time series data store

4. Which of the following is not a use case for Azure Data Factory?

a) Connecting to various data sources and performing transformations

b) Storing data in a single relational database

c) Integrating with other Azure services

d) Performing real-time analytics

Answer: b) Storing data in a single relational database

5. Which of the following is not a locking strategy in databases?

a) Optimistic locking

b) Pessimistic locking

c) Atomicity

d) Consistency

Answer: d) Consistency

6. Which of the following is not a type of index in databases?

a) Primary index

b) Secondary index

c) Self-join function

d) Hash keys

Answer: d) Hash keys

7. Which of the following is not a type of data stored in a document data store?

a) Customer details

b) Product details

c) Employee details

d) Invoice details

Answer: c) Employee details

8. What is no benefit of using Azure SQL warehouses for search and abstract data?

a) High availability

b) Ability to perform complex queries

c) Cost savings

d) Low latency

Answer: b) Ability to perform complex queries

9. Which of the following is not a type of data mapping in Azure Data Factory?

a) Map-reduce operations

b) In-memory operations

c) SQL queries

d) Spark operations

Answer: c) SQL queries

10. Which of the following is not hardware used in cloud computing?

a) Infrastructure as a service (IaaS)

b) Platform as a service (PaaS)

c) Software as a service (SaaS)

d) Network as a service (Naas)

Answer: d) Network as a service (Naas)

11. Which of the following is not a resource in Azure Data Factory?

a) Link services

b) Pipeline

c) Output dataset

d) Input dataset

Answer: a) Link services

12. Which of the following is not integration runtime in Azure Data Factory?

a) Azure Auto-Resolve

b) Self-hosted

c) Azure SSIS

d) Azure Virtual Networks

Answer: d) Azure Virtual Networks

13. Which of the following is not a storage account in Azure?

a) Cosmos DB

b) Azure Data Lake Storage

c) Azure Blob Storage

d) Azure SQL Storage

Answer: c) Azure Blob Storage

Conclusion

Finally, companies use Azure Data Factory – a robust yet flexible pipeline-building tool – to handle and transform data from various sources.

When interviewing for jobs with Azure Data Factory, be ready to demonstrate your knowledge and abilities by answering critical questions about its fundamentals and more advanced topics, such as features and best practices.

By following the advice in this Azure data factory scenario-based questionsblog, you should have no trouble passing an Azure Data Factory interview and landing the job of your dreams!

Prep is critical when answering Azure data factory questions and answers, so take your time learning its features and best practices before attending interviews for this platform.

Following the advice in this article, you should feel more at ease during Azure data factory questions and answers, increasing your odds of landing the job of your dreams!

To be ready, practice answering questions beforehand by answering on your feet before arriving for interviews.

Keep yourself flexible so you can adapt rapidly on interview day.

May your interview go smoothly!