Professional Cloud DevOps Engineer (PCDOE)
Build real cloud skills with guided labs on AWS and Google Cloud. Practice in live environments with instant access to real cloud resources. No cloud account required.
2 labs available
In this advanced lab, you will learn how to design and implement a scalable and secure Google Cloud resource hierarchy that aligns with an enterprise’s organizational structure. You'll focus on creating projects and folders, applying organization policies, and setting up IAM roles to allow least privilege access control. This lab will help you prepare for real-world scenarios where you need to manage multiple environments efficiently.
In this lab, learners will design and implement a Continuous Integration/Continuous Deployment (CI/CD) pipeline on Google Cloud Platform. You will build a pipeline that spans multiple environments, such as development, testing, and production, using Cloud Build and Cloud Deploy. The pipeline will include advanced deployment strategies like canary and blue/green deployments. Additionally, you will focus on integrating security by implementing Artifact Registry with vulnerability scanning and Binary Authorization. This lab will also cover IAM roles and organization policies to ensure that teams can securely deploy applications at scale. Finally, learners will learn to automate repetitive tasks via Infrastructure as Code using Terraform, thereby enabling efficient environment bootstrapping.
2 labs available
In this lab, you will build a comprehensive CI/CD pipeline using Google Cloud services. You'll implement advanced deployment stages, manage artifacts, and integrate security measures to ensure a robust DevOps pipeline. The hands-on experience will prepare you for real-world challenges, enhancing your skills in managing continuous integration and delivery across hybrid and multi-cloud environments.
In this advanced lab, you will design and implement a CI/CD pipeline for an e-commerce company deploying services on Google Kubernetes Engine (GKE). The lab focuses on integrating Cloud Build and Cloud Deploy for automating build and deployment processes, implementing secure storage of sensitive information using Secret Manager, and applying security policies utilizing Binary Authorization. Emphasizing security and efficiency, you will also explore scaled deployments using GKE's Horizontal Pod Autoscaler (HPA) and implement strategies for assessing pipeline failures through advanced logging and monitoring. This lab simulates a typical medium-sized enterprise digital transformation scenario, where a legacy system is migrating to cloud-native architectures, requiring careful resource management to stay within operational budgets.
2 labs available
This lab focuses on enhancing the reliability and performance of a mission-critical service using Managed Instance Groups (MIGs). You'll learn to configure advanced autoscaling policies that align with SLAs, implement rolling updates for zero-downtime deployments, and integrate monitoring solutions for proactive incident detection. Gain skills in balancing performance and cost efficiency while maintaining high availability and reliability standards.
In this lab, you will deploy a scalable web application that adheres to Site Reliability Engineering (SRE) best practices on Google Cloud Platform. The lab will guide you through defining SLIs and SLOs, setting error budgets, and configuring autoscaling policies for your application. Additionally, you will implement CI/CD pipelines using Cloud Build and Cloud Deploy, ensuring a streamlined deployment process with monitoring and alerting integrations for proactive incident management.
1 lab available
This hands-on lab challenges you to implement a comprehensive logging and metrics management solution for a digital retail company. Using Google Cloud services like Cloud Logging, Cloud Monitoring, and the Managed Service for Prometheus, you'll configure data ingestion, create actionable dashboards, and set up alerting policies. Learn how to handle sensitive data, optimize logging costs, and create synthetic metrics for operational insights. By the end of the lab, you'll have deep experience managing observability in GCP, preparing you for real-world DevOps challenges and the Professional Cloud DevOps Engineer certification.
1 lab available
In this advanced lab, learners will simulate a high-demand environment and optimize compute resources in Google Cloud to ensure cost-effective, reliable operations. You'll employ strategies such as using Google Kubernetes Engine (GKE) for autoscaling workloads, adopting Spot VMs for unreliable workloads, and tuning resources to match demand precisely using Google’s resource recommendation tools.
