Azure Data Factory Practice Exam

Azure Data Factory Practice Exam

Azure Data Factory is Microsoft’s cloud-based tool that helps businesses collect, organize, and move data from different sources into one place. Think of it like a factory line for data—taking raw information from different systems, cleaning it up, and delivering it in a usable form. This makes it easier for organizations to analyze information, generate reports, and make better business decisions without manual effort.

This certification is all about learning how to build and manage these data pipelines on Azure. You’ll explore how to connect data from on-premises systems, cloud platforms, or external applications and then transform it into insights. By mastering Azure Data Factory, professionals can help companies turn scattered information into powerful knowledge.

Who should take the Exam?

This exam is ideal for:

• Data Engineers 
• ETL Developers 
• Cloud Engineers 
• Data Analysts 
• BI Developers 
• IT Professionals 

Skills Required

• Basic understanding of cloud computing
• Knowledge of databases (SQL/NoSQL)
• Knowledge of ETL (Extract, Transform, Load) concepts
• Awareness of data security and compliance
• Problem-solving and logical workflow design

Knowledge Gained

• Creating and managing data pipelines in Azure
• Connecting on-premises and cloud-based data sources
• Transforming and cleaning data for analytics
• Automating workflows for efficiency
• Monitoring and troubleshooting data pipelines
• Supporting advanced analytics and BI reporting

Course Outline

The Azure Data Factory Exam covers the following topics - 

1. Introduction to Azure Data Factory

• What is Azure Data Factory
• Benefits of Data Integration on Azure
• Key Use Cases

2. Core Concepts and Architecture

• Pipelines and Activities
• Datasets and Linked Services
• Integration Runtimes

3. Data Ingestion & Integration

• Connecting On-Premises Data
• Cloud-to-Cloud Data Movement
• Hybrid Data Integration

4. Data Transformation

• Mapping Data Flows
• Data Wrangling
• Using Azure Databricks with ADF

5. Scheduling & Automation

• Triggers and Scheduling Pipelines
• Orchestration of Complex Workflows
• Dependency Handling

6. Security & Compliance

• Managing Access with Azure AD
• Data Encryption and Masking
• Compliance Standards

7. Monitoring & Optimization

• Monitoring with Azure Monitor
• Debugging and Troubleshooting Pipelines
• Performance Tuning

8. Real-World Applications

• Enterprise Data Warehousing
• Business Intelligence Data Prep
• Big Data and AI Integration