Azure Data Factory Practice Exam
Azure Data Factory Practice Exam
About Azure Data Factory Exam
The Azure Data Factory exam is designed for individuals aiming to demonstrate their proficiency in developing and managing data integration solutions using Azure Data Factory (ADF) services. ADF is a cloud-based data integration service that allows users to create data-driven workflows for orchestrating and automating data movement and transformation. This exam validates your ability to design, build, and operationalize data integration solutions using ADF, as well as your expertise in working with various Azure services for data engineering and analytics.
Skills Required
The exam assesses the following key skills:
- Designing and implementing data storage strategies, including data lakes, SQL and NoSQL databases.
- Working with different Azure data storage services (Azure Blob Storage, Azure SQL Database, etc.).
- Creating data pipelines for data ingestion and transformation.
- Creating data transformation pipelines using Data Flow in Azure Data Factory.
- Developing and implementing data movement strategies with Azure Data Factory.
- Utilizing various connectors for extracting data from different sources and destinations.
- Using data flow activities to manage and transform data within ADF pipelines.
- Implementing triggers and scheduling for automated pipeline execution.
- Managing data workflow dependencies and ensuring seamless data movement.
- Creating and managing complex data pipelines using ADF for automating tasks.
- Implementing monitoring solutions to track pipeline performance and troubleshoot errors.
- Using Azure Monitor and Azure Data Factory's monitoring tools for tracking the health of data workflows.
- Implementing alerts and notifications to ensure efficient monitoring of data pipelines.
- Configuring authentication and authorization for accessing data sources and destinations.
- Implementing role-based access control (RBAC) and managing security policies for pipelines and data.
- Ensuring secure data transfer and encryption in transit and at rest.
Who should take the Exam?
The Azure Data Factory exam is primarily intended for:
- Data engineers who are responsible for integrating, transforming, and managing large volumes of data in Azure.
- Cloud Architects and Engineers
- Developers who work with Azure services to build data processing and integration pipelines.
- Data analysts who work with large datasets and need to automate or transform data for analytics purposes.
- Individuals with experience in software development or other technical fields who are looking to specialize in cloud-based data integration.
- Those who want to gain expertise in Azure Data Factory and other Azure services to manage, process, and integrate data effectively for analytics and business intelligence.
Course Outline
The Azure Data Factory Exam covers the following topics -
Domain 1 - Introduction – Building Your First Azure Data Pipeline
- Course Overview
- Introduction to Azure Data Factory (ADF)
- Discussion of Requirements and Technical Architecture
- Register for a Free Azure Account
- Create a Data Factory Resource
- Set Up a Storage Account and Upload Data
- Create a Data Lake Gen 2 Storage Account
- Install Storage Explorer
- Build Your First Azure Data Pipeline
Domain 2 - Metadata-Driven Ingestion
- Overview of Metadata-Driven Ingestion
- High-Level Strategy
- Create an Active Directory User
- Assign the Contributor Role to the User
- Disable Security Defaults
- Set Up the Metadata Database
- Install Azure Data Studio
- Create Metadata Tables and Stored Procedures
- Reconfigure Existing Data Factory Artifacts
- Set Up a Logic App for Email Notifications
- Modify Data Factory Pipeline to Include Email Notifications
- Create Linked Services for Metadata Database and Email Datasets
- Create a Utility Pipeline for Email Notifications
- Explanation of the Email Recipients Table
- Explanation of the Get Email Addresses Stored Procedure
- Modify Ingestion Pipeline to Use Email Utility Pipeline
- Monitor the Triggered Pipeline
- Make Email Notifications Dynamic
- Dynamic Logging of Pipeline Information
- Add a New Logging Method for the Main Ingestion Pipeline
- Modify Pipeline Logging to Only Send Failure Alerts
- Create Dynamic Datasets
- Source to Target Data Transfer – Part 1
- Source to Target Data Transfer – Part 2
- Explanation of the Source to Target Stored Procedure
- Add Orchestration Pipeline – Part 1
- Add Orchestration Pipeline – Part 2
- Address Duplicate Batch Ingestions
- Review the Pipeline Log and Related Tables
- Understand the GetBatch Stored Procedure
- Understand the Set Batch Status and GetRunID Procedures
- Set Up an Azure DevOps Git Repository
- Publish Data Factory to Azure DevOps
Domain 3 - Event-Driven Ingestion
- Introduction
- Read Plan for Azure Storage
- Create Finance Container and Upload Files
- Create Source Dataset
- Write Plan for Data Lake – Raw Data
- Set Up Finance Container and Directories
- Create Sink Dataset
- Data Factory Pipeline Overview
- Create Data Factory and Read Metadata
- Apply Filter for CSV Files
- Add Dataset to Read Files
- Add the For Each CSV File Activity and Test Ingestion
- Define Event-Based Trigger Plan
- Enable Event Grid Provider
- Delete File and Set Up Event-Based Trigger
- Create Event-Based Trigger
- Publish Code to Main Branch and Activate Trigger
- Trigger Event-Based Ingestion