Professional Cloud DevOps Engineer certification video training course by prepaway along with practice test questions and answers, study guide and exam dumps provides the ultimate training package to help you pass.

Google Cloud Professional DevOps Engineer Certification (GCP)

Course Overview

The GCP Professional DevOps Engineer Certification course is designed to equip IT professionals with the knowledge and skills to implement DevOps practices on Google Cloud Platform. This course provides hands-on experience with CI/CD pipelines, site reliability engineering, monitoring, and automation. You will learn how to design, build, and manage scalable, reliable applications using GCP tools.

The course emphasizes practical application of DevOps principles. Students will work with Google Kubernetes Engine, Cloud Build, Cloud Monitoring, and other GCP services. By the end of the course, participants will be prepared to take the official certification exam with confidence.

This course balances theory with practical exercises. It is suitable for learners who want to enhance their DevOps skills while leveraging cloud-native tools.

Course Description

This training course guides you through the essential DevOps practices for Google Cloud. You will explore automation, continuous integration, continuous delivery, and monitoring in a cloud environment. The course emphasizes real-world application, helping you implement solutions for complex infrastructure challenges.

You will gain expertise in managing cloud resources, configuring CI/CD pipelines, and optimizing system performance. The course also covers reliability engineering concepts to ensure high availability of applications. Emphasis is placed on security, cost management, and scalable architecture.

Throughout the course, students will engage in hands-on labs and exercises. These exercises simulate real-world scenarios to reinforce learning. You will deploy containerized applications, manage infrastructure as code, and monitor system health in a live environment.

Who This Course Is For

This course is ideal for IT professionals, DevOps engineers, cloud architects, system administrators, and developers looking to specialize in DevOps on Google Cloud.

It is suitable for those with some cloud experience who want to deepen their skills in automation, deployment, and system reliability. Professionals seeking to improve their efficiency in managing cloud infrastructure will find this course valuable.

Beginners with basic understanding of cloud computing and software development can also benefit. The course provides foundational concepts and builds toward advanced DevOps practices.

Prerequisites and Requirements

Students should have basic knowledge of cloud computing concepts. Familiarity with Linux systems, networking, and programming is recommended. Prior experience with cloud platforms or DevOps tools is advantageous but not mandatory.

Knowledge of containers, Kubernetes, and scripting can enhance learning. Understanding version control systems like Git is beneficial. Students should be comfortable with command-line interfaces and basic automation tasks.

Hardware and software requirements include a computer capable of running virtual machines or containerized environments. A Google Cloud account is necessary for hands-on labs and exercises. Internet access and a modern web browser are required.

Learning Outcomes

After completing this course, participants will be able to design, build, and manage CI/CD pipelines on GCP. They will implement automation strategies, monitor applications, and ensure system reliability. Students will be able to optimize cloud infrastructure for performance, cost, and scalability.

Graduates of this course will have the skills to deploy containerized applications using Google Kubernetes Engine. They will configure monitoring tools, troubleshoot issues, and apply best practices for DevOps workflows.

Students will also gain insight into site reliability engineering practices, including incident management, alerting, and system optimization. They will be prepared to take the GCP Professional DevOps Engineer certification exam.

Course Modules

Introduction to GCP and DevOps

Learn the fundamentals of Google Cloud Platform. Explore DevOps principles and practices. Understand the role of automation, continuous integration, and continuous delivery in modern software development.

Continuous Integration and Continuous Delivery

Explore CI/CD concepts. Build pipelines using Cloud Build and other GCP services. Implement automated testing, deployment, and rollback strategies. Learn best practices for managing source code and artifacts.

Containerization and Orchestration

Gain expertise in Docker and Kubernetes. Deploy containerized applications on Google Kubernetes Engine. Understand orchestration, scaling, and management of containers in cloud environments.

Infrastructure as Code

Learn how to manage infrastructure using Terraform and Deployment Manager. Implement automated provisioning, configuration management, and infrastructure versioning. Ensure reproducible and scalable infrastructure deployments.

Monitoring and Logging

Implement monitoring strategies using Cloud Monitoring and Logging. Learn to track application performance, detect anomalies, and configure alerts. Understand observability and its role in site reliability engineering.

Security and Compliance

Explore security best practices in DevOps workflows. Implement IAM roles, policies, and secrets management. Understand compliance requirements and how to maintain secure cloud infrastructure.

Reliability and Performance Optimization

Learn techniques for ensuring high availability and performance. Study load balancing, autoscaling, and fault-tolerant designs. Implement strategies to optimize cost and resource utilization.

Hands-On Labs and Projects

Engage in practical exercises and labs. Deploy real-world applications on GCP. Apply DevOps principles to automate workflows, monitor systems, and manage infrastructure.

Continuous Integration and Continuous Delivery Deep Dive

Continuous Integration (CI) is the practice of automatically integrating code changes into a shared repository. CI ensures that new code is tested and validated frequently. Google Cloud provides Cloud Build as a fully managed CI service. Students will learn to configure build triggers, automate tests, and produce deployable artifacts.

Continuous Delivery (CD) extends CI by automating application deployment. Using CD, teams can deploy changes to production environments rapidly and reliably. Cloud Build, combined with Artifact Registry and Cloud Deploy, allows DevOps engineers to manage versioned releases, implement canary deployments, and perform blue-green deployments.

CI/CD pipelines improve collaboration between development and operations teams. They reduce integration issues, minimize manual errors, and accelerate release cycles. Students will gain hands-on experience building pipelines that include automated unit testing, integration testing, and deployment verification.

Containerization and Kubernetes Orchestration

Containerization standardizes application environments, enabling consistency across development, testing, and production. Docker is the primary containerization tool taught in this course. Students will learn to build container images, manage versions, and push images to Google Container Registry.

Kubernetes orchestrates containers at scale. Google Kubernetes Engine (GKE) provides a managed Kubernetes environment. Students will learn to create clusters, deploy applications, configure pods and services, and implement scaling policies.

Advanced topics include rolling updates, self-healing pods, and stateful workloads. Students will also explore Kubernetes networking, including services, ingress controllers, and load balancing. Understanding GKE’s auto-scaling capabilities is critical for cost-efficient, resilient deployments.

Infrastructure as Code Principles

Infrastructure as Code (IaC) allows engineers to provision and manage cloud resources through code. This approach ensures reproducibility, version control, and consistency. Google Cloud supports IaC via Terraform and Deployment Manager.

Students will learn to define infrastructure in declarative templates. Terraform modules enable reusable components for networks, compute instances, storage buckets, and Kubernetes clusters. Deployment Manager templates help automate Google Cloud resources using YAML or Python scripts.

IaC practices are integrated into CI/CD pipelines to ensure infrastructure changes undergo the same testing and validation as application code. This approach reduces risk and simplifies rollback in case of failure.

Monitoring, Logging, and Observability

Monitoring and logging are essential for maintaining application reliability. Google Cloud Monitoring allows engineers to track metrics, set alerts, and visualize system health. Cloud Logging collects logs from applications, services, and infrastructure components.

Observability combines monitoring, logging, and tracing to provide a complete view of system behavior. Students will learn to use Cloud Trace and Cloud Profiler to analyze latency, bottlenecks, and resource consumption.

Hands-on exercises include creating dashboards, setting alerting policies, and simulating incident responses. Students will understand the importance of proactive monitoring and how to integrate observability into DevOps workflows.

Security in DevOps

Security is integrated throughout the DevOps lifecycle. Students will learn the principles of DevSecOps, which emphasizes embedding security into CI/CD pipelines. Identity and Access Management (IAM) in GCP allows fine-grained control over resources.

Secrets management is taught using Secret Manager, enabling secure storage and retrieval of sensitive information. Students will implement secure container practices, including image scanning and vulnerability management.

Compliance and audit logging are also covered. Engineers will learn to generate logs for regulatory compliance, detect unauthorized access, and implement automated security checks in pipelines.

Reliability Engineering and High Availability

Site Reliability Engineering (SRE) principles guide the design of reliable, scalable systems. Students will learn techniques to maintain high availability, reduce downtime, and respond effectively to incidents.

Load balancing and autoscaling are critical components. Google Cloud provides Global Load Balancers and Managed Instance Groups to ensure traffic distribution and automatic scaling. Fault tolerance strategies, such as multi-region deployment and redundant services, are emphasized.

Incident response workflows are explored in depth. Students will practice configuring alerting policies, on-call schedules, and automated remediation steps. Postmortem analysis techniques help teams learn from failures and improve system reliability.

Cost Management and Optimization

Managing cloud costs is a crucial DevOps responsibility. Students will learn to monitor usage, optimize resource allocation, and reduce waste. GCP tools like Cost Management and Billing Reports provide visibility into spending patterns.

Techniques include right-sizing compute instances, using preemptible VMs for non-critical workloads, and optimizing storage costs with tiered storage solutions. Students will learn to implement automated cost alerts and integrate cost efficiency into CI/CD workflows.

Hands-On Labs and Real-World Projects

Hands-on labs reinforce the concepts learned in the course. Students will deploy microservices to GKE, implement CI/CD pipelines using Cloud Build, and manage infrastructure with Terraform.

Projects include building a complete end-to-end application pipeline, integrating automated testing, and configuring monitoring and alerting. Students will practice incident response simulations and optimize deployments for cost and performance.

Real-world scenarios emphasize collaboration, automation, and reliability. Students will gain experience with version control, container management, deployment strategies, and infrastructure automation.

DevOps Culture and Collaboration

Understanding DevOps culture is as important as mastering tools. Students will learn the values of collaboration, shared responsibility, and continuous improvement. Teams that embrace DevOps principles deliver software faster and with higher reliability.

Topics include agile methodologies, communication practices, and feedback loops. Students will explore how DevOps reduces silos between development and operations, fostering a culture of accountability and innovation.

Advanced Google Kubernetes Engine Practices

Google Kubernetes Engine (GKE) is a cornerstone of the DevOps engineer role on GCP. Advanced practices include managing multi-cluster environments, implementing cluster federation, and configuring network policies. Students will learn how to maintain consistent configurations across multiple clusters.

High-availability workloads require careful planning. Advanced topics include pod disruption budgets, taints and tolerations, and node auto-repair. These ensure that applications remain available during maintenance or unexpected failures.

Students will also explore GKE security features such as workload identity, private clusters, and binary authorization. These features help protect workloads, enforce policies, and prevent unauthorized container deployments.

Multi-Cloud and Hybrid Cloud Strategies

Many organizations adopt multi-cloud or hybrid cloud strategies to avoid vendor lock-in and improve resilience. Students will learn how GCP integrates with other cloud providers using Anthos. Anthos allows management of applications across on-premises and multiple cloud environments.

Key skills include configuring hybrid clusters, deploying consistent policies across clouds, and managing networking and security in multi-cloud setups. Students will gain hands-on experience with Anthos Service Mesh for secure service-to-service communication.

Multi-cloud strategies also involve cost management and workload optimization. Students will learn to analyze performance metrics, balance workloads across clouds, and implement failover strategies.

Automation Frameworks and Scripting

Automation is essential for modern DevOps workflows. Students will learn to write scripts using Python, Bash, or Go to automate deployment, monitoring, and incident response. Automation reduces manual errors and improves efficiency.

Frameworks such as Terraform, Ansible, and Cloud Deployment Manager are taught for infrastructure provisioning. Students will integrate these frameworks into CI/CD pipelines, enabling automated, repeatable, and version-controlled deployments.

Advanced automation practices include self-healing systems, automated rollback on failure, and automated scaling. Students will gain practical experience building pipelines that handle infrastructure and application lifecycle tasks automatically.

Advanced Monitoring and Observability

Beyond basic monitoring, advanced observability focuses on distributed tracing, metrics correlation, and anomaly detection. Students will learn to use Cloud Trace, Cloud Profiler, and Operations Suite for in-depth system analysis.

Predictive monitoring and alerting help prevent outages before they occur. Students will explore techniques for proactive problem detection and automated incident remediation. Observability dashboards are customized for different stakeholders, including developers, SREs, and management.

Students will also implement log aggregation, error tracking, and service-level indicators (SLIs) to improve reliability. Integration with PagerDuty or other incident management systems is covered.

Security Automation and Compliance

Advanced DevOps requires automated security checks. Students will implement CI/CD security gates, automated vulnerability scanning, and compliance checks using Security Command Center.

Identity and access management is enforced across services. Students will learn to manage service accounts, configure least-privilege roles, and rotate credentials. Automated auditing ensures that all changes are tracked and compliant with organizational policies.

Security policies are applied consistently across multi-cloud and hybrid environments. Students will understand how to integrate security into pipelines without slowing down delivery.

Disaster Recovery and Incident Response

Preparing for failures is a critical aspect of DevOps. Students will learn to design disaster recovery plans, including data backup, replication, and failover strategies. Multi-region deployments improve resilience and reduce downtime.

Incident response workflows are automated wherever possible. Students will simulate outages, analyze root causes, and implement lessons learned. Tools like Cloud Monitoring, Cloud Logging, and Alerting Policies are used to trigger automated recovery actions.

Students will also explore postmortem processes, focusing on documenting incidents, identifying improvements, and preventing recurrence. These practices are aligned with SRE principles.

Performance Optimization and Cost Efficiency

Optimizing performance and managing costs are integral to DevOps. Students will analyze resource utilization, identify bottlenecks, and optimize workloads for efficiency.

Techniques include right-sizing instances, optimizing container resource limits, and tuning database performance. Cost management tools in GCP allow tracking usage and predicting expenditures. Automated scaling and workload distribution reduce unnecessary spending.

Students will also explore caching strategies, content delivery optimization, and database indexing techniques to improve application responsiveness.

Advanced CI/CD Practices

Advanced CI/CD involves multi-stage pipelines, environment promotion, and feature flag deployment. Students will learn to build pipelines that include staging, pre-production, and production environments.

Integration with testing frameworks allows automated unit, integration, and end-to-end testing. Blue-green and canary deployments are practiced to reduce risk during production releases.

Students will also explore rollback strategies and pipeline monitoring to detect failures early. Automated notifications and logging ensure visibility across the development lifecycle.

Exam Preparation Strategies

This course also prepares students for the official GCP Professional DevOps Engineer Exam. Key strategies include reviewing exam guides, practicing hands-on labs, and simulating exam scenarios.

Students are encouraged to focus on practical experience, as GCP exams emphasize real-world problem-solving over theoretical knowledge. Reviewing case studies, troubleshooting exercises, and monitoring scenarios helps reinforce learning.

Time management during the exam is critical. Students will learn to approach multiple-choice questions, scenario-based questions, and performance-based questions systematically.

Capstone Projects and Assessment

Capstone projects integrate all course modules. Students will design a full DevOps workflow on GCP, including automated infrastructure provisioning, CI/CD pipelines, containerized deployments, monitoring, and incident response.

Projects simulate enterprise-level scenarios, requiring students to balance performance, security, reliability, and cost. Assessment is based on practical implementation, documentation, and adherence to best practices.

Hands-on labs ensure students are ready for real-world challenges. Projects are graded for both technical accuracy and alignment with DevOps principles, reinforcing the skills needed for certification success.

Real-World DevOps Case Studies

Understanding practical application is critical for DevOps engineers. In this section, students analyze real-world case studies of companies implementing DevOps on GCP. These case studies cover scenarios such as migrating monolithic applications to microservices, managing high-traffic workloads, and automating deployments across multiple regions.

Students will learn how to identify challenges in infrastructure management, performance bottlenecks, and security risks. Each case study emphasizes problem-solving techniques, best practices, and lessons learned. Students will examine how teams implemented monitoring, CI/CD pipelines, and infrastructure as code to achieve faster and more reliable releases.

Through case studies, students gain insights into decision-making processes for selecting the right GCP services for specific workloads. They also study cost optimization strategies implemented by organizations in production environments. This approach reinforces theoretical knowledge with practical application.

Advanced Troubleshooting Techniques

Troubleshooting is a core skill for DevOps engineers. Students will learn systematic approaches for diagnosing issues in applications, infrastructure, and CI/CD pipelines. Techniques include analyzing logs, examining metrics, performing root cause analysis, and using distributed tracing tools.

Students will practice identifying common errors in Kubernetes clusters, such as failed pod deployments, misconfigured network policies, and resource contention. They will also learn to troubleshoot CI/CD failures, container image errors, and deployment rollback issues.

Advanced troubleshooting includes proactive identification of potential problems before they impact production. Students will use monitoring alerts, anomaly detection, and predictive analytics to detect performance degradation. This proactive approach minimizes downtime and improves system reliability.

Scaling Strategies for Cloud-Native Applications

Scaling is essential for handling fluctuating workloads. Students will learn to design systems that scale horizontally and vertically on Google Cloud. Horizontal scaling involves adding more instances, pods, or nodes, while vertical scaling increases resources allocated to existing instances.

GCP services such as Google Kubernetes Engine, Cloud Load Balancing, and Managed Instance Groups provide automated scaling capabilities. Students will configure autoscaling policies based on CPU utilization, memory consumption, or custom metrics.

Strategies for scaling databases, caching layers, and storage solutions are also covered. Students will explore sharding, replication, and partitioning techniques for databases. Caching strategies using Memorystore and Cloud CDN improve performance and reduce load on backend systems.

Scaling considerations also include cost optimization. Students learn to balance performance, availability, and budget constraints while designing scalable solutions. Automation and monitoring ensure that scaling decisions are applied effectively and without manual intervention.

Multi-Team Collaboration and DevOps Culture

DevOps success depends on effective collaboration between teams. Students will explore strategies for breaking down silos between development, operations, security, and QA teams. Practices such as shared ownership of code, transparent communication, and continuous feedback are emphasized.

Collaboration tools, version control systems, and CI/CD pipelines are integrated to support cross-functional teams. Students learn to implement shared repositories, automated testing, and deployment pipelines that maintain consistency across teams.

DevOps culture emphasizes continuous improvement. Students will study metrics-driven feedback loops, incident retrospectives, and performance reviews. These practices encourage teams to iterate quickly, reduce errors, and enhance system reliability.

Students also learn how to implement DevOps principles in large organizations with distributed teams. Managing permissions, automating workflows, and enforcing standardized practices across teams is explored in depth.

Cloud-Native Application Patterns

Cloud-native architecture is designed to leverage cloud features for scalability, reliability, and flexibility. Students will study patterns such as microservices, serverless functions, event-driven design, and service meshes.

Microservices architecture breaks applications into small, independent components. Students will learn to deploy microservices on GKE, manage inter-service communication, and handle service discovery. Event-driven design involves using Pub/Sub, Cloud Functions, and Cloud Run to respond to events asynchronously.

Service meshes like Istio provide observability, security, and traffic management for microservices. Students will learn to configure routing, load balancing, and policy enforcement using service mesh capabilities.

Cloud-native patterns also include designing for resiliency. Techniques such as circuit breakers, retries, and graceful degradation help applications maintain availability during partial failures. Students will apply these patterns in lab exercises to simulate real-world scenarios.

Automated Testing Strategies

Automated testing is a critical component of DevOps workflows. Students will learn to implement unit tests, integration tests, end-to-end tests, and load tests in CI/CD pipelines. Automated testing ensures code quality and reduces the risk of defects reaching production.

Students will explore testing frameworks compatible with Google Cloud services. Techniques for mocking dependencies, testing containerized applications, and validating infrastructure as code are included.

Performance and load testing are essential for understanding system behavior under stress. Students will use tools such as JMeter and Cloud Load Testing to simulate high traffic conditions and evaluate system performance.

Automated testing integrates with deployment pipelines to enforce quality gates. Students learn to configure pipelines that block deployments if tests fail, ensuring that only stable and validated code reaches production.

Continuous Deployment Best Practices

Continuous deployment automates the delivery of applications to production. Students will learn deployment strategies including blue-green deployments, canary releases, and rolling updates.

Blue-green deployment involves maintaining two production environments. Traffic is switched from one environment to another during deployment, minimizing downtime and risk. Canary releases deploy changes to a small subset of users before full rollout, allowing for validation and rollback if issues arise.

Students will also study rolling updates, which gradually replace instances of an application with updated versions. Combined with monitoring and automated rollback, these strategies ensure smooth and safe deployments.

Deployment pipelines are integrated with monitoring, logging, and alerting systems. Students will configure pipelines to trigger automated notifications and remediation actions based on system health.

Incident Management and Postmortems

Incident management is a key responsibility for DevOps engineers. Students will learn structured approaches for handling incidents, minimizing downtime, and restoring service quickly.

The incident lifecycle includes detection, escalation, response, resolution, and postmortem analysis. Students will use GCP monitoring and alerting tools to detect anomalies and initiate response workflows.

Postmortems focus on understanding root causes, documenting lessons learned, and implementing preventive measures. Students will learn to create blameless postmortems that foster a culture of learning and continuous improvement.

Incident management also involves coordination with multiple teams. Students will study communication protocols, on-call rotations, and incident reporting to ensure efficient response across the organization.

Exam Readiness and Practice

Preparing for the GCP Professional DevOps Engineer exam requires practical experience and structured review. Students will follow a comprehensive exam guide, covering all exam objectives and scenarios.

Practice exams simulate real-world conditions. Students will answer scenario-based questions, multiple-choice questions, and performance-based labs. This approach builds confidence and helps identify areas that need reinforcement.

Reviewing lab exercises, troubleshooting scenarios, and case studies reinforces learning. Students are encouraged to document key concepts, commands, and configuration patterns for last-minute revision.

Time management is critical during the exam. Students will practice pacing, prioritizing questions, and applying systematic problem-solving strategies to maximize scores.

Capstone Integration Projects

Capstone projects integrate all learned skills into a single comprehensive workflow. Students will design, implement, and operate a complete DevOps solution on Google Cloud.

Projects include building end-to-end CI/CD pipelines, deploying microservices on GKE, implementing monitoring and alerting, ensuring security and compliance, and optimizing for cost and performance.

Students will document processes, create dashboards, simulate incidents, and perform recovery exercises. Capstone assessments are based on practical implementation, adherence to best practices, and alignment with DevOps principles.

The capstone experience ensures students are prepared for both real-world challenges and the certification exam. It bridges theory and practice, giving learners confidence in their ability to manage complex cloud-native systems.

Continuous Learning and Career Growth

DevOps is a constantly evolving field. Students are encouraged to continue learning beyond the course. Google Cloud releases new services, features, and best practices regularly. Staying updated ensures ongoing relevance and career growth.

Professional networking, attending conferences, contributing to open-source projects, and participating in online communities strengthen skills and industry knowledge. Certification demonstrates competence but continuous improvement is essential for long-term success.

Career opportunities include roles such as DevOps engineer, cloud engineer, site reliability engineer, cloud architect, and automation engineer. Mastery of GCP DevOps practices opens doors to leadership and specialized technical positions.

Prepaway's Professional Cloud DevOps Engineer video training course for passing certification exams is the only solution which you need.