This project was created as a hands-on-practice for preparing for the az-204 exam. It demonstrates how to use, configure and deploy Azure App Services. Examples are based on real-world scenarios, and can be used as a "baseline" reference. The project is split into four iterations where each iteration are standalone projects with different scopes and learning outcomes.

Additional exam relevant questions are included in the QA.md file.

• Azure subscription
• Terraform
• Docker technology ( Docker Desktop , Podman or Colima )
• .NET and .NET SDK
• Python

• Basic understanding of cloud computing concepts.
• Familiarity with command-line interfaces (bash/PowerShell).
• Basic understanding of web applications and HTTP.
• Git fundamentals (for Iteration 3).
• No prior Azure experience required, concepts are explained as we go.

• Terraform configuration for provisioning the necessary resources.
• A Python or .NET application with necessary code and functionality for testing.
• Various scripts for provisioning, building, and test automation.
• A comprehensive README file with sections for:
  • Learning Goals
  • Project description
  • Implementation steps
  • General theoretical overview of the topics covered, anchored in the az-204 syllabus
  • Relevant Azure CLI commands
  • General findings and heads-ups

Azure App Service represents the platform-as-a-service (PaaS) approach to web application hosting. With App Service, Microsoft handles the underlying infrastructure, OS updates, security patches, and scaling mechanisms, allowing developers to focus on application code.

Organizations choose App Service to accelerate development velocity and reduce operational overhead. Traditional server management requires dedicated IT staff to handle patching, monitoring, and scaling. App Service eliminates these responsibilities while providing enterprise-grade features like automatic backups, SSL certificate management, and integrated monitoring.

Furthermore, the service particularly excels for organizations with fluctuating traffic patterns. Instead of over-provisioning servers to handle peak loads, App Service can automatically scale resources up or down based on actual demand. This elasticity can result in significant cost savings compared to maintaining dedicated infrastructure.

• Create Azure App Service Web Apps - Provision App Service Plans and Web Apps using Terraform.
• Configure TLS/SSL - Implement HTTPS, custom domains, and certificate management.
• Application Settings Management - Configure environment variables, connection strings, and API settings.
• Deploy containerized solutions - Package and deploy applications using Docker containers to Azure Container Registry.
• Basic monitoring setup - Enable diagnostic logging and understand App Service logs.

• Application Insights integration - Enable comprehensive application performance monitoring and telemetry.
• Configure diagnostic logging - Set up application logs, web server logs, and detailed error messages.
• Custom metrics and alerts - Implement custom telemetry, tracking and automated alerting for production issues.
• Performance monitoring - Create dashboards and use metrics to enable identification of bottlenecks.
• Troubleshooting workflows - Practice using logs, metrics, and Application Insights to diagnose production issues.

• Configure deployment slots - Create STAGING and PRODUCTION environments for safe, zero-downtime deployments.
• Slot-specific vs. Shared settings - Understand and configure "sticky settings" for environment specific configuration.
• Automated CI/CD pipelines - Implement GitHub Actions workflows for automated deployments to staging on pull requests.
• Slot swap operations - Master manual promotion from staging to production.
• Rollback procedures - Implement emergency rollback workflows for "failed" production deployments.
• Blue-green deployment patterns - Understand how slot swaps enable instant rollback and A/B testing scenarios.

• Implement autoscaling - Configure automated scaling based on metrics and schedules.
• CPU-based autoscaling - Set up reactive scaling rules based on compute resource utilization.
• HTTP Queue Length scaling - Implement proactive scaling based on request queue depth for better responsiveness.
• Schedule-based scaling - Configure predictive scaling for known traffic patterns (business hours, seasonal loads, etc.).
• Platform limitations discovery - Documentation of real-world challenges with containerized workloads and metric collection.
• Cost optimization strategies - Reflection on balancing performance requirements with cost constraints through scaling.