20 Most Frequently Asked Azure Data Factory Interview Questions - Introduction

Sep 25, 2026

A set of interview-focused questions covering core concepts and practical scenarios in Azure Data Factory (ADF). The content introduces ADF’s role in cloud-based data ingestion and ETL workflows, then expands into foundational topics such as data pipeline creation, supported activity types, annotations, integration runtime, storage fundamentals, and ETL steps. It also includes an example project scenario and begins intermediate-level discussion on scheduling and parameterized pipeline runs.

LanguageEnglish

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Reading Time15 min

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Introduction

Basic Level Azure Data Factory Questions

Intermediate Level Interview Questions

20 Most Frequently Asked Azure Data Factory Interview Questions Introduction Azure data factory (ADF) is a cloud-based data ingestion and ET L ( Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation . Azure data factory helps organizations across the globe in making critical business decisions by collecting data from various sources such as e-commerce websites, supply chains, logistics, healthcare, etc . , transforming that data into a usable and trusted resource using multiple operations like filtering, concatenation, sorting, etc . , and loads that data into a destination store. Source: GitHub This article was published as a part of the Data Science Blogathon . Basic Level Azure Data Factory Questions Q1 . What is Azure Data Factory? ADF is a cloud-based data ingestion and ET L ( Extract, Transform, Load) Azure service. ADF helps organizations across the globe in making critical business decisions by building complex ET L processes and scheduled event-driven workflows to process data which later can be used by various reporting tools for storytelling purposes . Source: Microsoft Q2 . How Azure Data Factory Makes the Process of Creating a Data Pipeline Easy? ADF makes the process of creating a data pipeline easy by providing built-in connectors for data ingestion and orchestration, giving various activity options to perform operations such as copying data, for-each loop, look-up, etc . , validating, publishing and monitoring pipelines, continuous integration, and continuous deployment support to the pipelines . Q3 . What are the Different Types of Activities Supported by Azure Data Factory? Below are the different types of activities supported by ADF: Data Movement Activities: Activities used to move data from one data source to another in a data pipeline are known as Data movement activities . For example, copy activity can be used to copy data from ADLS to Azure SQL. Data Transformation Activities: Activities used to perform data transformation in a data pipeline are known as Data transformation activities . Data Flow Activity, Azure Functions Activity, Databricks Notebook Activity, etc . , are examples of data transformation activities . Control Activities: Activities used to build conditional, sequential, or iterative conditional logic in a data pipeline are known as control activities . Lookup Activity, Until Activity, For-Each Activity, etc . , are examples of control activities . Source: [docs. microsoft. com](docs. microsoft. com) Q4 . Solve the Project Scenario based on Question 1 . Your data team is building an ETL pipeline for a client. You want to generate output files from Azure Data Factory which are optimized for read-heavy analytical workload s and support the columnar format. What should be the file format of output files? The generated output files should have Parquet format as Parquet stores data in columns and are optimized for read-heavy analytical workloads . Q5 . What are Annotations in Azure Data Factory? Annotations are additional informative tags that help in filtering and searching data factory resources such as datasets, pipelines, linked services, etc . For example, if you are working as a team lead for a large data processing project for a client ABC that uses ADF containing 10 pipelines . To avoid confusion in the data processing sequence, we can label each pipeline with its primary purpose: ingest, transform, or load using annotations . When we are monitoring pipelines, these annotations must be available to perform searching, grouping, and filtering. Q6 . Why do we need Azure Data Factory? Azure Data Factory is a cloud-based data integration service that enables you to create, schedule, and manage data pipelines for ingesting, preparing, and transforming data from various sources to various destinations . It’s useful for ETL ( Extract, Transform, Load) processes and data m

FAQs

What is Azure Data Factory (ADF) and what does it do?

ADF is a cloud-based data ingestion and ETL tool. It orchestrates and automates data movement and transformation from multiple sources into a destination store for trusted, usable resources.

What types of activities does Azure Data Factory support?

ADF supports data movement activities (e.g., copy), data transformation activities (e.g., Data Flow, Azure Functions, Databricks Notebook), and control activities (e.g., Lookup, Until, For-Each) for conditional, sequential, or iterative logic.

What is the integration runtime in Azure Data Factory?

Integration Runtime is the compute infrastructure ADF uses to deliver integration capabilities across different network environments. It enables secure connectivity to on-premises data sources, including via self-hosted setup.

Sep 25, 2026

Uploaded by Sophia Brooks