Top Azure Data Factory Interview Questions with Answers

Here are the top 30 Azure Data Factory interview questions with answers:

1. What is Azure Data Factory?

Ans:-Azure Data Factory is a fully managed, cloud-based data integration service that helps you create, schedule, and monitor data-driven workflows. It can be used to ingest data from a variety of sources, transform it, and load it into a variety of destinations.

2. What are the components of Azure Data Factory?

Ans:-The main components of Azure Data Factory are:

Pipelines: Pipelines are the logical containers for activities. They define the sequence of steps that need to be executed to complete a data transformation task.
Activities: Activities are the individual steps that are executed in a pipeline. They can be used to ingest data, transform data, or load data.
Datasets: Datasets are the logical representations of data sources. They can be used to point to a file, a database, or a cloud storage account.
Linked services: Linked services are the connections to data sources. They are used to provide the credentials and connection information that Azure Data Factory needs to access the data source.
Triggers: Triggers are used to control the execution of pipelines. They can be used to execute pipelines on a schedule, or in response to an event.
Control flows: Control flows are used to control the order of execution of activities within a pipeline.

3. What are the benefits of using Azure Data Factory?

It is a fully managed service, so you do not need to worry about managing the underlying infrastructure.
It is scalable, so you can easily add or remove resources as needed.
It is secure, so you can be confident that your data is safe.
It is integrated with other Azure services, so you can easily build end-to-end data solutions.

4. What are the limitations of Azure Data Factory?

Ans:- Some of the limitations of Azure Data Factory include:

It can be complex to learn and use.
It is not as flexible as some other data integration tools.
It does not support all data sources.

5. What are the different types of triggers in Azure Data Factory?

Ans:- There are three types of triggers in Azure Data Factory:

Schedule triggers: These triggers execute pipelines on a schedule, such as every day or every hour.
Event triggers: These triggers execute pipelines in response to an event, such as a file being created or a database table being updated.
Manual triggers: These triggers are executed manually by a user.

6. What are the different types of computing environments in Azure Data Factory?

Ans:- There are two types of computing environments in Azure Data Factory:

On-demand compute environment: This is a fully managed compute environment that is created when a pipeline is executed and destroyed when the pipeline is finished.
Dedicated compute environment: This is a dedicated compute environment that is created and managed by the user.

7. What are the different types of data sources that can be used with Azure Data Factory?

Ans:-Azure Data Factory can be used with a variety of data sources, including:

Files
Databases
Cloud storage accounts
Azure Data Lake Storage
Azure Synapse Analytics
Other Azure services

8. What are the different types of data transformations that can be performed with Azure Data Factory?

Ans:-Azure Data Factory can be used to perform a variety of data transformations, including:

Filtering
Joining
Aggregation
Formatting
Encryption
Deduplication

9. What are the different types of output destinations that can be used with Azure Data Factory?

Ans:-Azure Data Factory can be used to load data into a variety of output destinations, including:

Files
Databases
Cloud storage accounts
Azure Data Lake Storage
Azure Synapse Analytics
Other Azure services

10. What are the best practices for using Azure Data Factory?

Ans:- Some of the best practices for using Azure Data Factory include:

Use a consistent naming convention for your pipelines, activities, and datasets.
Use variables to store frequently used values.
Use parameters to make your pipelines more dynamic.
Use logging to track the execution of your pipelines.
Use version control to track changes to your pipelines.

Explain the concept of a Data Factory linked service.

A Data Factory-linked service is a configuration that defines the connection information to an external data store or service. It includes connection strings, authentication methods, and other settings.

11. What are Integration Runtimes in Azure Data Factory?

Ans:-Integration routines are the compute infrastructure used by Azure Data Factory to execute activities. They can be located in Azure, on-premises, or in a virtual network.

12. How can you perform data partitioning in Azure Data Factory?

Ans:- Data partitioning involves dividing data into smaller segments for parallel processing. Azure Data Factory supports partitioning techniques like round-robin and hash partitioning.

Explain the concept of event-based triggers in Azure Data Factory.

Event triggers in Azure Data Factory allow you to start pipelines based on external events. These events can be file creations, HTTP requests, or other triggers.

13. What is Data Factory Data Flow Debugging?

Ans:- Data Flow Debugging enables you to troubleshoot your data transformations within Data Flows. You can inspect intermediate data at various stages of processing.

How can you move data between different regions using Azure Data Factory?

To move data between different Azure regions, you can configure linked services for the respective regions and use them in your pipelines to perform cross-region data transfers.

14. What is the purpose of ADF Service?

Ans:- ADF’s primary purpose is to handle data replication across local and remote, relational and non-relational data sources. In addition, ADF may replicate data between these different types of sources. Additionally, the ADF Service can be used to modify the incoming data to cater to the requirements of a particular organization. Ingestion of data can be accomplished using ADF Service either as an ETL or an ELT tool. This makes it a vital component of the vast majority of Big Data solutions. Sign up for Azure Training in Hyderabad to gain an in-depth understanding of the several advantages offered by ADF Service.

15. If you want to use the output by executing a query, which activity shall you use?

Ans:-Look-up activity can return the result of executing a query or stored procedure.

The output can be a singleton value or an array of attributes, which can be consumed in subsequent copy data activity, or any transformation or control flow activity like ForEach activity.

16. Can we pass parameters to a pipeline run?

Ans:-Yes, parameters are a first-class, top-level concept in Data Factory. We can define parameters at the pipeline level and pass arguments as you execute the pipeline run on demand or using a trigger.

17) What are the key differences between the Mapping data flow and the Wrangling data flow transformation activities in Azure Data Factory?

Ans:-In Azure Data Factory, the main difference between the Mapping data flow and the Wrangling data flow transformation activities is as follows:

The Mapping data flow activity is a visually designed data transformation activity that facilitates users to design graphical data transformation logic. It doesn’t need the users to be expert developers. It is executed as an activity within the ADF pipeline on an ADF fully managed scaled-out Spark cluster.
On the other hand, the Wrangling data flow activity is a code-free data preparation activity. It is integrated with Power Query Online to make the Power Query M functions available for data wrangling using spark execution.

18) Is it possible to define default values for the pipeline parameters?

Ans:- Yes, we can easily define default values for the parameters in the pipelines.

19) How can you access the data using the other 80 Dataset types in Azure Data Factory?

Ans:-Azure Data Factory provides a mapping data flow feature that allows Azure SQL database, Data Warehouse, Delimited text files from Azure Blob Storage, or Azure Data Lake storage to generate tools natively for source and sink. We can use copy activity to state data from any other connectors and then execute the data flow activity to transform data.

20) Can an activity in a pipeline consume arguments that are passed to a pipeline run?

Ans:- Every activity within the pipeline can consume the parameter value passed to the pipeline and run with the @parameter construct.

21. What are some of the advantages of carrying out a lookup in the Azure Data Factory?

Ans:- Within the ADF pipeline, the Lookup activity is utilized rather frequently for configuration lookup. It includes the data set in its initial form. In addition to this, the output of the activity can be used to retrieve the data from the dataset that served as the source. In most cases, the outcomes of a lookup operation are sent back down the pipeline to be used as input for later phases.

To provide a more detailed explanation, the lookup activity in the ADF pipeline is responsible for retrieving data. You may only utilize it in a manner that is appropriate for the process you are going through. You have the option of retrieving just the first row, or you may select to obtain all of the rows in the dataset depending on the query.

22. What sorts of variables are supported by Azure Data Factory and how many different kinds are there?

Ans:- Variables are included in the ADF pipeline so that values can be temporarily stored in them. Their application is almost entirely equivalent to that of variables in programming languages. There are two types of operations that are used to assign and change the values of variables. These are set variables and add variables.

The Azure data factory makes use of two different categories of variables:

In Azure, the pipeline’s constants are referred to as system variables. Pipeline ID, Pipeline Name, Trigger Name, etc. are all instances.
The user is responsible for declaring user variables, which are then utilized by the logic of the pipeline.

23. What is the connected service offered by the Azure Data Factory, and how does it operate?

Ans:- In Azure Data Factory, the connection method that is utilized to join an external source is referred to as a “connected service,” and the phrase is used interchangeably. It not only serves as the connection string, but it also saves the user validation data.

The connected service can be implemented in two different ways, which are as follows:

ARM approach.
Azure Portal.

24. What is meant to be referred to when people use the phrase “breakpoint” in conjunction with the ADF pipeline?

Ans:- The commencement of the testing step of the pipeline is indicated by the placement of a debugging breakpoint. Before committing to a particular action, you can make use of breakpoints to check and make sure that the pipeline is operating as it should.

Take the following example into consideration to get a better understanding of the concept: you have three activities in your pipeline, but you only want to debug through the second one. In order to be successful in this endeavor, a breakpoint needs to be established for the second task. By simply clicking the circle located at the very top of the activity, you will be able to add a breakpoint.

25. What are the different activities you have used in Azure Data Factory?

Ans:- Here you can share some of the significant activities if you have used them in your career, whether your work or college project. Here are a few of the most used activities :

Copy Data Activity to copy the data between datasets.
Each activity for looping.
Get Metadata Activity that can provide metadata about any data source.
Set Variable Activity to define and initiate variables within pipelines.
Lookup Activity to do a lookup to get some values from a table/file.
Wait for Activity to wait for a specified amount of time before/in between the pipeline run.
Validation Activity will validate the presence of files within the dataset.
Web Activity to call a custom REST endpoint from an ADF pipeline.

26. How can I schedule a pipeline?

Ans:- You can use the time window or scheduler trigger to schedule a pipeline. The trigger uses a wall-clock calendar schedule, which can schedule pipelines periodically or in calendar-based recurrent patterns (for example, on Mondays at 6:00 PM and Thursdays at 9:00 PM).

Currently, the service supports three types of triggers:

Tumbling window trigger: A trigger that operates on a periodic interval while retaining a state.
Schedule Trigger: A trigger that invokes a pipeline on a wall-clock schedule.
Event-Based Trigger: A trigger that responds to an event. e.g., a file getting placed inside a blob.
Pipelines and triggers have a many-to-many relationship (except for the tumbling window trigger). Multiple triggers can kick off a single pipeline, or a single trigger can kick off numerous pipelines.

27. When should you choose Azure Data Factory?

Ans:- One should consider using Data Factory-

When working with big data, there is a need for a data warehouse to be implemented; you might require a cloud-based integration solution like ADF for the same.
Not all team members are experienced in coding and may prefer graphical tools to work with data.
When raw business data is stored at diverse data sources, which can be on-prem and on the cloud, we would like to have one analytics solution like ADF to integrate them all in one place.
We would like to use readily available data movement and processing solutions and be light regarding infrastructure management. So, a managed solution like ADF makes more sense in this case.

28. How can you access data using the other 90 dataset types in the Data Factory?

Ans:-The mapping data flow feature allows Azure SQL Database, Azure Synapse Analytics, delimited text files from Azure storage account or Azure Data Lake Storage Gen2, and Parquet files from blob storage or Data Lake Storage Gen2 natively for source and sink data source.

Use the Copy activity to stage data from any other connectors and then execute a Data Flow activity to transform data after it’s been staged.

29. Can a value be calculated for a new column from the existing column from mapping in ADF?

Ans:- We can derive transformations in the mapping data flow to generate a new column based on our desired logic. We can create a new derived column or update an existing one when developing a derived one. Enter the name of the column you’re making in the Column textbox.

You can use the column dropdown to override an existing column in your schema. Click the Enter expression textbox to start creating the derived column’s expression. You can input or use the expression builder to build your logic.

30. How is the lookup activity useful in the Azure Data Factory?

Ans:-In the ADF pipeline, the Lookup activity is commonly used for configuration lookup purposes, and the source dataset is available. Moreover, it retrieves the data from the source dataset and then sends it as the activity output. Generally, the output of the lookup activity is further used in the pipeline for making decisions or presenting any configuration as a result.

Simply put, lookup activity is used for data fetching in the ADF pipeline. The way you would use it entirely relies on your pipeline logic. Obtaining only the first row is possible, or you can retrieve the complete rows depending on your dataset or query.