Azure Data Factory Interview Prep: 400+ Most Asked Q&A: 2025

Azure Data Factory Interview Prep: 400+ Most Asked Q&A: 2025 free download

Crack Azure Data Factory Interview with Confidence: 400+ Interview Questions[ Conceptual + Scenario] with Answers: 2025

Azure Data Factory Interview Prep: 400+ Most Asked Q&A (2025 Edition) is a structured and in-depth course designed to help you master the core concepts, tools, and real-world applications of Azure Data Factory (ADF). This course is specifically tailored for job seekers preparing for Azure Data Engineer, BI Developer, or Cloud Integration interviews. It features over 400 interview-style questions, scenario-based drills, and targeted explanations to prepare you for the types of questions asked in technical interviews.

We begin with a strong foundation through Introduction to Azure Data Factory, where you'll explore what ADF is, how it supports cloud-based and hybrid data movement, and how it compares with tools like SSIS and Databricks. You'll also understand ADF’s serverless architecture and the core building blocks such as pipelines, activities, datasets, and linked services.

Covered in:
1. Introduction to Azure Data Factory

• ADF basics and use cases

• Architecture overview

• ADF vs SSIS vs Databricks

• Supported workloads: batch, real-time, hybrid

Once the foundation is clear, we dive into Pipelines and Activities, where you learn how to design, build, and control your data workflows. You will understand the types of activities available, how to manage control flow using conditions and loops, and how to use parameters, variables, and expressions to create dynamic and reusable pipelines.

Covered in:
2. Pipelines and Activities

• Creating pipelines in ADF Studio

• Types of activities (Copy, Control, Data Flow, External)

• Parameters, variables, and expressions

• Error handling techniques and retry policies

Next, you’ll gain deep insight into Data Movement using the Copy Activity. This includes defining sources and sinks, managing different file formats, mapping data, and tuning for performance. This section answers many performance and configuration questions typically asked during interviews.

Covered in:
3. Data Movement (Copy Activity)

• Source and sink configuration

• File formats and schema mapping

• Performance tuning techniques

We then shift focus to Data Transformation using Mapping Data Flows, where you will learn how to design transformations visually using the built-in data flow engine. Topics like joins, filters, conditional splits, aggregations, and debugging strategies will be covered in detail.

Covered in:
4. Data Transformation (Mapping Data Flows)

• Creating and configuring data flows

• Common transformation types

• Sink strategies and formats

• Debugging and monitoring flows

In the next section, Linked Services, Datasets, and Integration Runtime, you will learn how to connect ADF to various data sources securely and efficiently. You’ll understand how IRs function across cloud and on-premises environments, and how datasets define data structure.

Covered in:
5. Linked Services, Datasets, and Integration Runtime

• Linked services configuration and parameterization

• Tabular and file-based datasets

• Types of Integration Runtime: Azure, Self-hosted, Azure-SSIS

• Network and security settings

Then comes Triggers and Scheduling, essential for automating and orchestrating workflows. You’ll learn how to implement time-based, event-based, and windowed triggers, including how to pass parameters and handle timezone settings.

Covered in:
6. Triggers and Scheduling Pipelines

• Trigger types (schedule, tumbling window, event)

• Recurrence settings and time zones

• Trigger parameters and monitoring

Security is critical in cloud data platforms. The Security, Identity & Access Management section equips you with knowledge of ADF’s security model, how to use role-based access control, integrate with Managed Identity, and securely manage secrets with Azure Key Vault.

Covered in:
7. Security, Identity & Access Management

• RBAC and access policies

• Managed Identity (MSI)

• Azure Key Vault integration

• Network security strategies

The next section focuses on Monitoring, Debugging, and Logging to help you effectively troubleshoot pipeline failures and analyze performance bottlenecks. You’ll explore how to use Azure Monitor, log diagnostics, and configure alerting mechanisms.

Covered in:
8. Monitoring, Debugging, and Logging

• Pipeline monitoring and activity-level logs

• Debug mode in pipelines and data flows

• Setting alerts using Azure Monitor

• Retry and timeout configurations

Then you’ll explore how to build dynamic pipelines through Parameterization and Dynamic Content. This section helps you prepare for technical questions around reusability, modular design, and dynamic configurations across pipelines and linked services.

Covered in:
9. Parameterization and Dynamic Content

• Pipeline, dataset, and linked service parameters

• Use of system variables and expressions

• Reusability through templates and modular design

The course concludes with CI/CD and DevOps with ADF, where you’ll learn how to integrate ADF with Git, manage development through branches, deploy with ARM templates, and automate pipelines using REST APIs—key skills expected in modern cloud-based data engineering roles.

Covered in:
10. CI/CD and DevOps with ADF

• GitHub or Azure DevOps integration

• Live deployment via publishing

• ARM template deployment and automation

• Triggering pipelines via REST APIs

Whether you're preparing for an interview next week or trying to solidify your understanding of ADF through practical examples and interview drills, this course ensures that you walk in with clarity and confidence.