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One of Microsoft's most powerful cloud tools today is Azure Data Factory. (also known as ADF) If you want to expand your career in Microsoft Azure, you should also know about Azure Data Factory. It gathers business data and processes it to create actionable reports and information. Data Factory is an excerpt, transform and load (ETL) service developed to automate data transformation.
 
In this blog, we will look at the main Azure Data Factory interview questions that you should prepare before your job interview. The questions and answers here cover the basics, intermediate and advanced topics that could be useful for beginners, experienced, and professionals to master the interview. These questions also help in real-time azure data factory scenarios.
 
Below is a list of some of the most common azure data factory interview questions and their relevant answers. This blog also includes azure data bricks interview questions.
 

Q1. What is Azure Data Factory?


Azure Data Factory is a comprehensively controlled cloud-based tool from Microsoft that automates data transformation and movement. This data integration ETL service collects raw data and transforms it into useful information. Through ADF, you can create pipelines, which are data- and schedule-driven workflows.
 

Q2. What are the features of Azure Data Factory? Explain briefly.


Pipeline: Represents all activities within a logical container.
Dataset: Datasets are pointers to data used in channel activities.
Data Flow Mapping: This means the data transformation UI logic.
Activity: In Data Factory channels, this refers to the execution you can use to transform and consume data.
Trigger: The trigger exposes the execution time of the track.
Linked Service: Represents the connection string for data sources used in channel activities.
Control Flow: Regulates the flow of execution of pipeline activities.
 

Q3. Why do we need Azure Data Factory?


If you go through any Microsoft Azure tutorial, you will find Data Factory mentioned in all of them. In today's data-driven world, data flows from multiple sources. Each source transmits or routes data using different methods in many formats. When this information needs to be transferred over the cloud or other storage platforms, it must be effectively managed before sharing. Therefore, this raw data from numerous sources should be cleaned, filtered, and transformed to remove unwanted components before sharing.

Because it revolves around data transfer, businesses should determine that data is collected from multiple sources and stored in a shared location. You can also achieve data storage and transformation through conventional warehouses. However, they have some limitations. Traditional warehouses have customized applications to manage their processes. However, it is time-consuming, and integrating each feature can be hectic. Therefore, you need a way to automate the process or ensure that workflows are developed appropriately. With Azure Data Factory, all these procedures can be coordinated more conveniently.
 

Q4. What is the limit on the integration runtimes you can do if any?


You can perform any integration runtime incidents in Azure Data Factory. There is no limit here. However, there is a boundary to the number of virtual machine cores that the integration runtime can use for each SSIS package implementation subscription. Anyone pursuing Microsoft Azure certification should know and understand each of these terms.
 

Q5. What is Azure Data Factory Integration Runtime?


Data Factory uses a safe and secure computing infrastructure called the Integration Runtime. It offers data integration capabilities across different environments. In addition, it ensures that these activities are performed in areas as close as possible to your data stores.
 

Q6. What type of computing environment does Azure Data Factory support?


Azure Data Factory supports two types of computing environments, namely:

Self-built atmosphere: With the help of Azure Data Factory, you create and monitor this computing environment yourself.
On-demand environment: It is an environment that is a fully managed add-on from Azure Data Factory. A cluster is created to perform the transformation activity.
 

Q7. What are the steps entangled in creating an ETL process in Data Factory?


Consider the case where you try to retrieve data from an SQL database. Any data that needs to be processed goes through processing before going to the Azure Data Lake Store, where it is stored. The steps to create the ETL are given here.
Imagine that we are using a dataset of automobiles. Start by creating a linked service for your SQL Server database or any source data store.

Then you need a linked service for your target store's Data Lake Store.
Next, create a dataset to store the data.
Set the channel and then add the copy activity.
Finally, insert the trigger and schedule your channel.
 

Q8. Enter two levels of security in Azure Data Lake Storage Gen2.


Azure Access Control Lists: Specifies a data object a user can read, write, or perform. ACLs are friendly to Linux or Unix users because they are POSIX compliant.

Azure Role-Based Access Control: Includes various built-in Azure roles such as Contributor, Owner, Reader, and more. It is allocated for two reasons - to determine who can monitor the service and to enable the use of built-in data explorer tools.
 

Q9. What is Azure Table Storage?


Azure Table Storage is fast and efficient storage that allows users to store structured data in the cloud. This service offers a Keystore with designed schemes.
 

Q10. How many trigger types does Data Factory support?


Three trigger types are supported in ADF. These are:

Tumbling Window Trigger: This trigger helps to trigger the ADF pipeline at cyclic intervals. The pipe state is maintained by the rotating window trigger.
Schedule Trigger: This trigger helps manage ADF channels following a wall clock schedule.
Event-based trigger: This trigger allows you to react to any event related to the blob store.
 

Q11. How can you create Azure Functions?


Azure Function is a solution for implementing small functional lines or code in a cloud environment. With Azure Functions, you can choose the programming languages ??of your choice. Users only pay when the code is run for the first time, which means a pay-per-use model is implemented. Features support languages ??like C#, F#, Java, PHP, Python, and Node.JS. In addition, Azure Functions also support constant integration and deployment. Businesses can design applications that don't require servers using Azure Functions.
 

Q12. Is it possible to pass pipeline run parameters?


Yes, it is possible to pass pipeline run parameters. You can define channels and pass arguments by starting the channel run with a trigger or on demand.
 

Q13. What requirements should you meet to run an ADF SSIS package?


To run an SSIS package in Azure Data Factory, you must create an SSISDB catalog and an SSIS IR hosted on a SQL database or Microsoft Azure-managed instance.
 

Q14. What is the purpose of Microsoft Azure Data Factory?


The primary goal of Data Factory is to orchestrate data replication between multiple non-relational and relational data sources hosted locally within enterprise data centers or cloud platforms. Data Factory Service is also reasonable for transforming received data and meeting business goals. In a typical Big Data solution, the Data Factory Service plays the role of an ETL or ETL tool that enables data to ingest.
 

Q15. Explain about Microsoft Azure Databricks.


Databricks is a fast, interoperable, and accessible platform based on Apache Spark and optimized for Microsoft Azure. Databricks was designed in collaboration with the founders of Apache Spark. Also, Databricks combines the best features of Azure and Databricks to help users accelerate innovation with a faster setup. Business analysts, data scientists, and engineers can collaborate more effectively and create an interactive workspace through these fluid workflows.
 
Winding Up
In this blog, we have seen some of the top interview questions for azure data factory.