Azure Data Factory Interview Questions

Azure Data Factory is a widely used cloud-based ETL and data integration service that contributes to the advancement of data engineering. Our Azure Data Factory interview questions blog offers the knowledge to give your best in your following job interview. The content furnished in this blog is suitable for freshers and experienced candidates.

Azure ADF Interview Questions And Answers

To make things clear, we have designed this ADF interview questions into three categories
  1. Basic ADF Interview Questions
  2. ADF Interview Questions For Experienced
  3. Scenario-Based Azure Data Factory Interview Questions

There are multiple concepts in Azure Data factory, to clarify, let's discuss concept-wise ADF interview questions and their simplified answers.

Basic Azure Data Factory (ADF) Interview Questions

1) What problem does ADF Solve?

We all know the importance of data in today's technology-driven world. Data is growing at an alarming rate, which can come from different sources and in various types. Getting the insights hidden in big data becomes complex. The data needs to undergo operations like combining data from various sources, data transformation, data orchestration, building workflows, and more.

It is challenging to perform all these operations using traditional tech tools. Azure data factors offer next-generation capabilities and simplify end-to-end data movement processes.

2) What is Azure Data Factory?

Azure Data Factory is a cloud-based platform specializing in data integration and ETL operations. It simplifies the process for the data engineers to transform data at scale and build workflows for data movement. 

Azure data factory streamlines building data pipelines to ingest data from diversified sources. It allows building fundamental to advanced ETL processes to transform data using modern computing services such as Azure data bricks, Azure SQL database, etc. Using ADF, you can publish data to data warehouse & BI tools. 

3) Is Azure Data Factory PaaS or Saas?

ADF is a PaaS ( Platform as a Service) offering specializing in data movement between on-premises & cloud and performs data transformation.

4) Define the ETL Process.

ETL is a core component of the Data engineering process and stands for Extract, Transform, and Load. In simple terms, it gathers data from different sources ( Extraction), turns data into the required format (transformation), and stores data in a defined target (loading).

5) What is Data Orchestration?

Data orchestration is an automated process where the data can be sourced from multiple sources and combined, and data is readily available for advanced data analysis tools.

6) What is Incremental Data loading?

Incremental data loading is a process where a comparison can be made between source data and target data. In a nutshell, when data is loaded to a target, it first makes a comparison with the source system to the target system and then loads only changed data, not all data.

Related Article: Azure Data Factory vs Databricks

7) Name the critical components of Azure Data Factory.

Following are some of the critical components of the ADF, which work together to move data from source to target and perform data transformation tasks.

The following are the critical components of ADF:

  • Pipelines
  • Datasets
  • Activities
  • Data Flows
  • Linked Services
  • Integration Runtimes

8) What is the Azure Data Factory Pipeline?

In Data engineering, a pipeline is a series of steps required for processing data. In a Data factory, a pipeline can contain activities that accomplish a portion of the work. All these activities construct a pipeline and accomplish a required task. A typical data pipeline comprises source, processing, and destination.  A data pipeline logically combines activities and automates them for data processing.

9) Explain about Datasets in Azure Data Factory.

Datasets help users to understand the data structures in a data store. Using this component, you can quickly point to the data you wish to use.

10) Define Azure Data Factory Activity.

In simple terms, an Activity is a task or step performed in a Pipeline. 

Following are the three types of Activities:

  • Control Activities
  • Data Movement Activities
  • Transformation Activities.

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ADF Interview Questions For Experienced

11) What is Data Flow in Azure Data Factory?

Data engineers mainly use the data flow component to build data transformation logic using a graphical approach. ADF offers two types of data flows:

  • Mapping Data Flow
  • Wrangling Data Flow

12) Explain Mapping data flow in ADF.

Mapping Data Flows in ADF allows data engineers to design transformation logic visually without writing code.

13) What is Wrangling Data Flow in ADF?

Data Wrangling allows developers to build Power Query mesh-ups usable in ADF data pipelines.

14) Explain ADF Linked Services.

These are connection strings containing information that helps us ADF connect to external sources.

15) What is Integration Runtime in ADF?

The Azure integration runtime (IR) is a powerful and default compute infrastructure used by Azure Data Factory Pipelines to support integration. It acts as a bridge between ADF Linked services and Activity.

16) What types of integration runtime are offered by ADF?

Following are the three different integration runtimes offered by ADF:

  • Azure
  • Azure-SSIS
  • Self-hosted

17) Name the triggers supported by ADF.

Below mentioned are the three triggers supported by ADF:

Tumbling Window Trigger: Executes Pipelines on Cycle intervals.
Schedule Trigger: Records response related to blob storage.
Event-based Trigger: Executes ADF Piplens based on timetable.

18) What is data lake storage?

A data lake is a data repository that securely stores and processes structured and semi-structured data. It stores large volumes of data and processes data irrespective of the data type & size.

19) What is Azure HDInsight?

It is an open-source analytics platform that simplifies the process of running big data frameworks. It enables data engineers to build optimized clusters for Spark, Kafka, LLAP, Hadoop, and HBase on Azure.

20) Is coding knowledge mandatory to work on Azure Data Factory?

Absolutely no. ADF has 90 + built-in connectors to transform data using mapping data flow activities.

ADF Scenario-Based Questions

21) How can you schedule a trigger in ADF?

In ADF, creating a trigger to run a pipeline periodically is possible.  You can create a scheduler and specify the time (start, recurring, end, etc.).  The next step is to attach a targeted pipeline. We can create single or multiple triggers on a single pipeline.

22) Can parameters be passed from one pipeline to another in ADF?

Yes, passing parameters from the Parent pipeline to the Child pipeline is possible. Following are the simple steps required to pass parameters:

  1. Add parameters to the parent pipeline
  2. Add parameters to the child pipeline
  3. Select the child pipeline and pass the parameter.

23) Is it possible to add default values to a parameter in the ADF Pipeline?

Yes, Adding default values to parameters in the pipeline is possible.

24) Which Version of ADF do you use to create data flows?

We can create data flows using the ADF V2 version.

25) What types of computing environments are supported by ADF to perform data transformation tasks?

Following are the two types of environments supported by ADF for Transformation tasks:

  • On-Demand Compute Environment
  • Bring your Environment

26) What is Copy Activity?

In ADF, you can use the copy activity component to perform data copying activities between the cloud and on-premises. This component can also publish data transformation and analysis for BI & application use. 

27) What Azure Copy activity supports the file formats?

Following are the file formats supported by copy activity:

  • Binary format
  • Avro format
  • Excel format
  • Delimited text format
  • JSON format
  • Excel format
  • Parquet format
  • ORC format
  • XML format

28) How do you use copy activity to copy data?

Following the three significant steps involved in copying data:

  • Create linked services for source & sync
  • Datasets creation for source & sink
  • Create a Pipeline and attach a copy activity.

29) Does Copy Activity support incremental data copying?

Yes, ADF & Synapse pipelines allow the users to load data incrementally.

30) Define Azure Table Storage?

Azure table storage is essential for storing large volumes of structured data.

Wrapping up:

So far, we have discussed some of the frequently asked Azure data factory interview questions, and many more questions will be added to this blog soon. Practice these questions before you attend any ADF interview & all the very best.

By Tech Solidity

Last updated on February 12, 2024