Top 50 Azure Data Factory Interview Questions and Answers

This post will cover the Top 30 Azure Data Factory Interview Questions. These are well-researched, up to date and the most feasible questions that can be asked in your very next interview. 

Azure Data Factory is a cloud-based ETL service for scaling out data Integration and transformation. It offers you to lift and shift existing SSIS packages on Azure. 

Topics of discussions: 

• Azure Data Factory Interview Questions
  • Azure Data Factory Interview Questions for Beginners
  • Azure Data Factory Interview Questions for Intermediates
  • Azure Data Factory Interview Questions for Experienced
• Conclusion

Azure Data Factory Interview Questions and Answers

I have divided Azure Data Factory Interview questions as per their difficulty level. Let’s dive right into these questions. 

Azure Data Factory Interview Questions for Beginners

Q1) What is Azure Data Factory?

Azure Data Factory is an integration and ETL service offered by Microsoft. You can create data-driven workflows to orchestrate and automate data movement. You can also transform the data over the cloud. It lets you create and run data pipelines that can help move and transform data and run scheduled pipelines. 

Q2) Why do we need Azure Data Factory? 

• Increased value
• Improved results of business processes
• Reduced overhead costs
• Improved decision making
• Increased business process agility

Q3) What do we understand by Integration Runtime?

Integration runtime is referred to as a compute infrastructure used by Azure Data Factory. It provides integration capabilities across various network environments.

A quick look at the Types of Integration Runtimes:

• Azure Integration Runtime – Can copy data between cloud data stores and send activity to various computing services such as SQL Server, Azure HDInsight, etc.
• Self Hosted Integration Runtime – It’s basically software with the same code as the Azure Integration Runtime, but it’s installed on your local system or virtual machine over a virtual network.
• Azure SSIS Integration Runtime – It allows you to run SSIS packages in a managed environment. So when we lift and shift SSIS packages to the data factory, we use Azure SSIS Integration Runtime.

Q4) What is the difference between Azure Data Lake and Azure Data Warehouse?

Azure Data Lake	Data Warehouse
Data Lakes are capable of storing data of any form, size, or shape.	A Data Warehouse is a store for data that has previously been filtered from a specific resource.
Data Scientists are the ones who use it the most.	Business professionals are the ones who use it the most.
It is easily accessible and receives frequent changes.	Changing the Data Warehouse becomes a very strict and costly task.
When the data is correctly stored, it determines the schema.	Before storing the data, the data warehouse defines the schema.
It employs the ELT (Extract, Load, and Transform) method.	It employs the ETL (Extract, Transform, and Load) method.
It’s an excellent tool for conducting in-depth research.	It is the finest platform for operational users.

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Q5) What is the limit on the number of Integration Runtimes?

There is no restriction on the number of integration runtime instances that can be used. However, the number of VM cores used by Integration runtime for SSIS package execution is limited to one per subscription.

Q6) What is Blob Storage in Azure?

Blob storage is specially designed for storing a huge amount of unstructured data such as text, images, binary data. It helps make your data available public globally. The most common use of blob storage is to stream audios and videos, store data for backup, analysis etc. You can also work with data lakes to perform analytics using blob storage. 

Q7) Difference between Data Lake Storage and Blob Storage.

Data Lake Storage	Blob Storage
It’s a big data analytics workload-optimized storage solution.	Blob Storage is a type of general-purpose storage that can be used in a variety of situations. It’s also capable of Big Data Analytics.
A hierarchical file system is used.	It’s based on a flat namespace object store.
Data is saved in Data Lake Storage as files within folders.	You can create a storage account with Blob Storage. The data is stored in containers in the storage account.
Batch, interactive, stream analytics, and machine learning data can all be stored in it.	Text files, binary data, media storage for streaming, and general-purpose data can all be stored on it.

Q8) Describe the process to create an ETL process in Azure Data Factory?

You can create an ETL process with a few steps. 

• Create a service for linked data store i.e. SQL Server Database. 
• Let’s consider you have a dataset for vehicles. 
• Now for this dataset, you can create a linked service for the destination store i.e. Azure Data Lake. 
• Then create a Data Set for Data Saving. 
• The next step is to create a pipeline and copy activity. When you are done with creating a pipeline, schedule a pipeline with the use of an added trigger.  

Q9) What is the difference between Azure HDInsight and Azure Data Lake Analytics?

Azure HDInsight	Azure Data Lake Analytics
It’s a Platform as a Service (PaaS) model.	It’s a SaaS (Software as a Service) model.
It needs configuring the cluster with predetermined nodes in order to process data. We can also process the data using languages like pig or hive.	It’s all about passing the data processing queries that have been written. To process the data set, Data Lake Analytics creates compute nodes.
HDInsight Clusters can be readily configured by users at their leisure. Users have unrestricted access to Spark and Kafka.	In terms of setting and customization, it does not offer a lot of options. However, Azure handles it for its users automatically.

Q 10) What are the top-level concepts of Azure Data Factory?

There are four basic top-level Azure Data Factory concepts:

• Pipeline – It acts as a transport service where many processes take place.
• Activities – It represents the stages of processes in the pipeline.
• Datasets – This is the data structure that holds our data.
• Linked Services – These services store information needed when connecting other resources or services. Let’s say we have a SQL server, so we need a connection string that is connected to an external device and we will mention its source and destination.

Q 11) What are the different types of triggers supported by Azure Data Factory?

Azure Data Factory supports three types of triggers:

1. Tumbling Window Trigger: This trigger executes pipelines in Azure Data Factory at recurring intervals. It is commonly used for maintaining pipeline states.
2. Event-based Trigger: The event-based trigger allows you to respond to events related to blob storage. You can create triggers when new blobs are added or when existing blobs are deleted.
3. Schedule Trigger: The schedule trigger executes pipelines based on a predetermined schedule, following the wall clock timetable. It allows for the automation of pipeline runs according to specified time intervals.

Q 12) Which cross-platform SDKs are available in Azure Data Factory for advanced users?

Azure Data Factory V2 offers a variety of rich cross-platform SDKs that allow advanced users to write, manage, and monitor pipelines using their preferred integrated development environment (IDE). Notable cross-platform SDKs for advanced users in Azure Data Factory include the following:

1. Python SDK
2. C# SDK
3. PowerShell CLI

Additionally, users have the option to interface with Azure Data Factory V2 using the documented REST APIs. These SDKs and APIs empower advanced users to leverage their expertise and work efficiently with Azure Data Factory, utilizing their preferred programming languages and tools.

Q 13) What is the role of “Datasets” in the ADF framework?

In the ADF framework, datasets serve as containers for inputs and outputs utilized by pipeline activities. A dataset represents the organization of data within a connected data store, which can be a file, folder, document, or any other type of data entity. For instance, an Azure blob dataset specifies the folder and container in blob storage from which a specific pipeline activity needs to read data for further processing. This information helps determine the data source for reading operations.

Q 14) What are the data sources used by Azure Data Factory?

Data sources in Azure Data Factory refer to the original or final storage locations of data that will be processed or utilized. Data can exist in various formats such as binary, text, comma-separated values (CSV), JSON files, and more. Data sources can include databases like MySQL, Azure SQL Database, PostgreSQL, as well as non-database entities like images, videos, audio files, Azure Data Lake Storage, and Azure Blob Storage. These examples demonstrate the range of data sources that can be leveraged within Azure Data Factory for data integration and processing tasks.

Q 15) What is the significance of ARM Templates in Azure Data Factory?

In Azure Data Factory, ARM Templates are JSON (JavaScript Object Notation) files used to define the infrastructure and configuration of data factory pipelines. This includes pipeline activities, linked services, datasets, and more. The content of the template closely resembles the code of our pipeline.

ARM templates prove useful when migrating pipeline code to higher environments like Production or Staging from Development, ensuring the code’s functionality is validated before deployment.

Azure Data Factory Interview Questions for Intermediates

Q 16) How can we schedule a pipeline?

We can schedule pipelines using a trigger. It follows a world clock calendar schedule. We can schedule pipelines periodically or calendar-based recurrent patterns. Here are the two ways:

• Schedule Trigger
• Window Trigger

Q 17) Is there any way to pass parameters to a pipeline run?

Yes absolutely, passing parameters to a pipeline run is a very easy task. Pipelines are known as the first-class, top-level concepts in Azure Data Factory. We can set parameters at the pipeline level and then we can pass the arguments to run a pipeline.

Check out: What is Azure?

Q 18) What is the difference between the mapping data flow and wrangling data flow transformation?

• Mapping Data Flow: This is a visually designed data conversion activity that allows users to design graphical data conversion logic without the need for experienced developers.
• Wrangling Data Flow: This is a code-free data preparation activity built into Power Query Online.

Q 19) How do I access the data using the other 80 Dataset types in Data Factory?

Dataflow mapping now enables Azure SQL databases, data warehouses, Azure Blob storage, or delimited text files in Azure Data Lake storage to build native build tools for source and receiver. You can use a copy operation to declare data from one of the other connectors, then you can run a data stream operation to transform the data.

Q 20) Explain the two levels of security in ADLS Gen2?

• Role-based Access Control – It includes built-in Azure rules such as reader, contributor, owner or customer roles. It is indicated for two reasons. The first is who can manage the service themselves, and the second is to provide users with built-in data mining tools.
• Access Control Lists – Azure Data Lake Storage specifies exactly which data objects users can read, write, or execute.

Q 21) What is the difference between the Dataset and Linked Service in Data Factory?

• Dataset: A reference to a datastore described by a linked service.
• Linked Service: Just a description of the connection string used to connect to the data store.

Q 22) What has changed from private preview to limited public preview regarding data flows?

Some of the things that have changed are mentioned below:

• There is no need to bring your own Azure Databricks clusters now.
• Data Factory will handle cluster creation and deletion.
• We can still use Data Lake Storage Gen 2 and Blob Storage to store these files. You may use the appropriate linked services. You may also use associated services that are appropriate for the services of the storage engines.
• Blob dataset and Azure Data Lake gen 2 storage split into delimited text and Apache Parquet dataset.

Q 23) Data Factory supports two types of compute environments to execute the transform activities. What are those?

Let’s take a look at the types.

• On-Demand Computing Environment – This is a fully managed environment provided by ADF. This type of calculation creates a cluster to perform the transformation activity and automatically deletes it when the activity is complete. 
• Bring your own environment – In this environment, use ADF to manage your computing environment yourself.

Q 24) What is Azure SSIS Integration Runtime?

Azure SSIS Integration is a fully managed cluster of virtual machines hosted in Azure and designed to run SSIS packages in your data factory. You can scale up SSIS nodes simply by configuring the node size, or you can scale out by configuring the number of nodes in the virtual machine cluster.

Q 25) What is required to execute an SSIS package in Data Factory?

You need to create an SSIS integration runtime and SSIS database catalog hosted on an Azure SQL database or an Azure SQL managed instance.

Also Check: Microsoft Certification Roadmap 2023.

Q 26) Do I need coding knowledge for Azure Data Factory?

No, coding knowledge is not required for Azure Data Factory. With Azure Data Factory, you can leverage its 90 built-in connectors and mapping data flow activities to transform data without the need for programming skills or knowledge of Spark clusters. It enables you to create workflows efficiently and quickly, simplifying the data integration and transformation process.

Q 27) What are the benefits of performing a lookup in Azure Data Factory?

Performing a lookup in Azure Data Factory offers several advantages. The Lookup activity is commonly used within ADF pipelines for configuration lookup, providing access to the dataset in its original form. The output of the Lookup activity can be used to retrieve data from the source dataset, and often these lookup results are passed downstream in the pipeline to serve as input for subsequent stages.

To delve further, the Lookup activity in ADF is responsible for data retrieval and can be customized based on the specific requirements of the process. It allows you to retrieve a single row or multiple rows from the dataset, depending on your query. This flexibility in data retrieval enhances the overall functionality and versatility of Azure Data Factory pipelines.

Q 28) What types of variables are supported in Azure Data Factory, and how many categories are there?

Azure Data Factory supports variables within its pipelines to temporarily store values, similar to variables in programming languages. There are two types of operations used to assign and modify variable values: set variables and add variables.

Azure Data Factory utilizes two different categories of variables:

1. System variables: These are predefined constants within the Azure environment, such as Pipeline ID, Pipeline Name, Trigger Name, and more. These variables are automatically available for use within the pipeline logic.
2. User variables: These variables are declared by the user and can be utilized in the pipeline’s logic as needed. User variables provide flexibility for customizing and enhancing the pipeline’s functionality.

With these variable types, Azure Data Factory enables effective data manipulation and flow control within pipelines.

Q 29) What is the connected service in Azure Data Factory, and how does it function?

In Azure Data Factory, the term “connected service” is used to describe the connection method used to integrate an external data source. It acts as the connection string and also securely stores user authentication data.

The connected service can be set up using two different approaches:

1. ARM approach: The connected service can be configured programmatically using Azure Resource Manager (ARM) templates, which provide a declarative way to define and manage Azure resources.
2. Azure Portal: Alternatively, the connected service can be set up and managed directly through the Azure Portal, offering a user-friendly graphical interface for configuring the connection.

By utilizing connected services, Azure Data Factory enables seamless integration with various external data sources, facilitating efficient data movement and processing within pipelines.

Q 30) What is the process for deploying code to higher environments in Data Factory?

The following steps outline the deployment process at a high level:

1. Create a feature branch to contain the code base.
2. Submit a pull request to merge the code into the Dev branch once it has been thoroughly tested.
3. Publish the code from the Dev branch to generate ARM templates.
4. This can trigger an automated CI/CD DevOps pipeline to promote the code to higher environments, such as Staging or Production.

Q 31) Which constructs in Data Factory are considered useful?

Here are some beneficial constructs available in Data Factory:

1. parameter: The @parameter construct allows each activity in the pipeline to utilize the parameter value passed to the pipeline.
2. coalesce: By using the @coalesce construct in expressions, null values can be gracefully handled.
3. activity: The @activity construct enables the consumption of activity outputs in subsequent activities.

Q 32) What is the purpose of copy activity in Azure Data Factory?

Copy activity is a widely used and popular feature in Azure Data Factory. It serves the purpose of ETL (Extract, Transform, Load) or migrating data from one source to another. During the data transfer, copy activity also allows for transformations to be performed. For instance, you can read data from a TXT/CSV file with 12 columns, but when writing to the target data source, you can choose to keep only seven columns by performing the necessary transformations. This enables sending only the required columns to the destination data source.

Q 33) What is the significance of a breakpoint in the ADF pipeline?

A breakpoint in the ADF (Azure Data Factory) pipeline allows for debugging and controlling the execution flow. By placing a breakpoint at a specific activity, you can halt the pipeline’s execution at that point during debugging. This is useful when you want to analyze or troubleshoot the pipeline’s behavior up to a certain activity. To add a breakpoint, simply click on the circle icon located at the top of the desired activity.

Azure Data Factory Interview Questions for Experienced

Q 34) What is Azure Table Storage?

Azure Table Storage is a service that helps users to store structured data in the cloud and also provides a Keystore with schemas designed. It is swift and effective for modern-day applications.   

Q 35) Can we monitor and manage Azure Data Factory Pipelines?

Yes, we can monitor and manage ADF Pipelines using the following steps:

• Go to the Data factory tab and click on the monitor and manage.
• Now click on the resource manager.
• You will be able to see pipelines, datasets, and linked services in a tree format. 

Q 36) An Azure Data Factory Pipeline can be executed using three methods. Mention these methods.

Methods to execute Azure Data Factory Pipeline:

• Debug Mode
• Manual execution using trigger now
• Adding schedule, tumbling window/event trigger

Q 37) If we need to copy data from an on-premises SQL Server instance using a data factory, which integration runtime should be used?

Self-hosted integration runtime should be installed on the on-premises machine where the SQL Server Instance is hosted. 

Q 38) What are the steps involved in the ETL process?

The ETL (Extract, Transform, Load) process follows four main steps:

• Connect and Collect – Helps move data to local and crowdsource data storage. Transform data using computing services such as HDInsight, Hadoop, Spark etc. 
• Publish -Useful for loading data into Azure data warehouses, Azure SQL databases, Azure Cosmos DB, and more.
• Monitor – Supports Azure Monitor, API and PowerShell, log analysis, and pipeline monitoring through the Azure portal health scope.

Q 39) Can an activity output property be consumed in another activity?

Yes. An activity output can be consumed in a subsequent activity with the @activity construct.

Q 40) What is the way to access data by using the other 90 dataset types in Data Factory?

For source and sink, the mapping data flow feature supports Azure SQL Database, Azure Synapse Analytics, delimited text files from Azure Blob storage or Azure Data Lake Storage Gen2, and Parquet files from Blob storage or Data Lake Storage Gen2.

Use the Copy action to stage data from any of the other connectors, then use the Data Flow activity to transform the data once it’s staged. For example, your pipeline might copy data into Blob storage first, then transform it with a Data Flow activity that uses a dataset from the source.

Q 41) Is it possible to calculate a value for a new column from the existing column from mapping in ADF?

In the mapping data flow, you can use derive transformation to generate a new column based on the logic you want. You can either create a new derived column or update an existing one when generating a derived column. Enter the name of the column you’re creating in the Column textbox.

The column dropdown can be used to override an existing column in your schema. Click the Enter expression textbox to start creating the derived column’s expression. You have the option of either inputting your expression or using the expression builder to create your logic.

Q 42) What is the way to parameterize column name in dataflow?

We can pass parameters to columns similar to other properties. Like in derived column customer can use $ColumnNameParam = toString(byName($myColumnNameParamInData)). These parameters can be passed from pipeline execution down to Data flows.

Q 43) In what way we can write attributes in cosmos DB in the same order as specified in the sink in ADF data flow?

Because each document in Cosmos DB is stored as a JSON object, which is an unordered set of name/value pairs, the order cannot be guaranteed.

Q 44) Is it possible to set default values for the parameters in the pipeline?

Yes, it is possible to set default values for parameters in the pipeline.

Q 45) Is it possible for an activity within a pipeline to utilize the arguments passed to a pipeline run?

Yes, it is possible for an activity within a pipeline to utilize the arguments passed to a pipeline run. This allows the activity to access and use the values of the arguments during its execution, providing flexibility and customization within the pipeline workflow.

Q 46) What is the method to address null values in the output of an activity?

The @coalesce construct in expressions can be used to handle null values effectively.

Q 47) Which version of Data Factory should I use to create data flows?

To create data flows, you should use Data Factory V2.

Q 48) What are the most valuable components among Data Factory’s building blocks?

1. The @parameter construct allows each activity within the pipeline to access and utilize parameter values provided to the pipeline.
2. The @coalesce construct in expressions offers an effective solution for handling null values.
3. The @activity construct enables the utilization of results obtained from one activity in another, enhancing the overall data processing capabilities of Data Factory.

Q 49) What is the concept of a “data flow map”?

In Azure Data Factory, mapping data flows refer to visual representations of data transformations. Data engineers can use mapping data flows to define data manipulation logic without writing any code. These data flows are then executed as activities within scaled-out Apache Spark clusters, which are part of Azure Data Factory pipelines. The scheduling, control flow, and monitoring capabilities of Azure Data Factory can be leveraged to operationalize data flow operations.

The process of data flow mapping is highly immersive and eliminates the need for scripting. The execution clusters responsible for data flow processing are managed by Azure Data Factory, allowing for massively parallel data processing. Azure Data Factory handles all coding tasks, including code interpretation, pathway optimization, and data flow execution.

Q 50) What is the purpose of Data Flow Debug?

Data Flow Debug in Azure Data Factory and Synapse Analytics allows for data flow troubleshooting and real-time monitoring of data transformation. The flexibility of the debug session benefits both the Data Flow design process and the execution of pipeline debugging.

Q 51) Can nested looping be achieved in Azure Data Factory?

Azure Data Factory does not provide direct support for nested looping within its looping activities (such as for each or until). However, it is possible to achieve nested looping by utilizing a workaround. This involves using a single looping activity (for each or until) that contains an execute pipeline activity, which in turn contains another loop activity. By implementing this structure, the outer looping activity indirectly invokes the inner loop activity, allowing for the realization of nested looping functionality.

Q 52) Is it possible to integrate Data Factory with machine learning data?

Absolutely, Data Factory enables seamless integration with machine learning data. With Data Factory, you can train and retrain models using machine learning data directly within your pipelines. Once trained, you can publish the model as a web service for further consumption and utilization.

Conclusion

Guys, no doubt there are a number of job offerings for Azure Data Engineers. And the jobs will increase drastically in the upcoming years as every other company is opting for cloud computing. But, how well you prepare for these opportunities is all what matters.

I have divided the latest Azure Data Factory interview questions as per their difficulty level. These Azure Data Factory interview questions will surely help you to get that extra benefit in an interview over other candidates. 

Related/References