Prerequisites

The Google Professional Cloud DevOps Engineer certification exam is designed for the Application Developers and Programmers who want to learn how to design, develop, and deploy Cloud applications. This test has no formal prerequisites. However, the individuals are recommended to have at least 3 years of industry experience, involving 1 or more years of experience with managing solutions on Google Cloud Platform.

Reference: https://cloud.google.com/certification/cloud-devops-engineer

Topics of Google Professional Cloud DevOps Engineer Exam

Candidates must know the exam topics before they start preparation because it will help them in hitting the core. Our Professional Cloud DevOps Engineer Dumps will include the following topics:

Applying site reliability engineering principles to a service

Construct feedback loops to decide what to build next
1.2 Manage service life cycle:
Manage a service (e.g., introduce a new service, deploy it, maintain and retire it)
Balance change, velocity, and reliability of the service:
Discover SLIs (availability, latency, etc.)
Toil automation
Define SLOs and understand SLAs
Agree to consequences of not meeting the error budget

Building and implementing CI/CD pipelines for a service

Configure deployment processes (e.g., approval flows)
Deployment to hybrid and multi-cloud environments with Anthos, Spinnaker, Kubernetes
Testing a new version with Spinnaker
Deployment strategies with Cloud Build, Spinnaker
Artifact versioning strategy with Cloud Build, Container Registry
Immutable artifacts with Container Registry
Design CI/CD pipelines:
Artifact repositories with Container Registry
CI/CD pipeline triggers with Cloud Source Repositories, Cloud Build GitHub App, Cloud Pub/Sub

Implementing service monitoring strategies

Understanding the logging exclusion vs. logging export
Sending application logs directly to Stackdriver API with Stackdriver Logging
Collecting logs from Compute Engine, GKE with Stackdriver Logging, Fluentd
Manage application logs:
Collecting GKE/Kubernetes metrics
Collecting third-party and structured logs with Stackdriver Logging, Fluentd
Selecting the options for logging export
Implementing a project-level / org-level export
Use metric explorer for ad hoc metric analysis
Viewing export logs in Cloud Storage and BigQuery
Set ACL to restrict access to audit logs with IAM, Stackdriver Logging
Implementing logs-based metrics
Collecting metrics from Compute Engine
Using basic vs. advanced logging filters
Viewing logs in the GCP Console
Sending logs to an external logging platform
Set ACL to restrict export configuration with IAM, Stackdriver Logging
Enabling VPC flow logs
Set ACL to allow metric writing for custom metrics with IAM, Stackdriver Monitoring
Enabling data access logs (e.g., Cloud Audit Logs)

Optimizing service performance

Interpret service mesh telemetry
Utilize Stackdriver Trace/Profiler to profile performance characteristics
identify resource costs
Consider network pricing
Troubleshoot network issues (e.g., VPC flow logs, firewall logs, latency, view network details)
Develop a plan to optimize areas of greatest cost or lowest utilization
Utilize Stackdriver to identify cloud resource utilization
Evaluate and understand user impact (Stackdriver Service Monitoring for App Engine, Istio)
Identify resource utilization levels
Manage preemptible VMs
TCO considerations
Work with committed-use discounts
Identify service performance issues:
Troubleshoot issues with the image/OS

Managing service incidents

Define roles (incident commander, communication lead, operations lead)
Rotate/hand over roles
Identify probable causes of service failure
Coordinate roles and implement communication channels during a service incident:
Manage stakeholder relationships
Avoid exhaustion/burnout
Record major changes in incident state (When mitigated? When all clear? etc.)
Provide regular status updates, internal and external
Handle requests for impact assessment
Scaling response team and delegation
Evaluate symptoms against probable causes; the rank probability of cause based on observed behavior
Perform an investigation to isolate the most likely actual cause
Establish communications channels (email, IRC, Hangouts, Slack, phone, etc.)
Identify alternatives to mitigate the issue

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Who should take the Google Professional Cloud DevOps Engineer exam

Individuals should pursue the Professional Cloud DevOps Engineer practice exam if they want to demonstrate their expertise and ability to design plan, and prototype a GCP DevOps, implement a GCP Virtual Private Cloud (VPC), implement DevOps security. It's perfect for DevOps engineers, systems administrators or operations team members, or simply any professional who wants in on this specific area of IT and cloud.

Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Implementing observability and troubleshooting	20%	- Optimize observability strategy 1. Reduce noise and prioritize signals 2. Integrate observability into development 3. Scale monitoring for large environments - Build monitoring and logging systems 1. Create dashboards and alerts 2. Collect and analyze metrics, logs, traces 3. Use Cloud Monitoring, Cloud Logging, Cloud Trace - Troubleshoot complex issues 1. Trace requests end-to-end 2. Isolate problems across layers 3. Analyze performance and latency
Applying site reliability engineering practices	18%	- Manage incidents and outages 1. Detect and respond to issues quickly 2. Perform postmortems and root cause analysis 3. Implement improvements to prevent recurrence - Define reliability goals 1. Establish SLIs, SLOs, SLAs 2. Use error budgets to balance change and stability 3. Align reliability with business needs - Design for resilience 1. Automate remediation and self-healing 2. Test failure scenarios and recovery 3. Plan redundancy and fault tolerance
Building and implementing CI/CD pipelines	25%	- Implement and secure pipelines 1. Automate build, test, and deployment stages 2. Manage pipeline secrets and credentials 3. Enforce security scanning and compliance checks - Design CI/CD pipelines 1. Integrate artifact management 2. Plan pipelines for apps, infrastructure, ML workloads 3. Support hybrid/multi-cloud deployment - Optimize and maintain pipelines 1. Monitor pipeline performance 2. Handle rollbacks and versioning 3. Improve speed and reliability
Optimizing performance and cost	17%	- Apply FinOps practices 1. Right-size resources and eliminate waste 2. Monitor and analyze cloud costs 3. Use spot instances and committed use discounts - Improve system performance 1. Tune applications and infrastructure 2. Use caching and content delivery 3. Optimize resource allocation - Balance cost, performance, and reliability 1. Plan capacity and scaling 2. Optimize over time 3. Make tradeoff decisions based on data
Bootstrapping and maintaining a Google Cloud organization	20%	- Set up governance and compliance 1. Configure audit logging and monitoring 2. Implement organization policies 3. Manage resource quotas and limits - Manage configuration and deployment 1. Enforce consistency across environments 2. Automate resource provisioning 3. Use Infrastructure as Code tools - Design resource hierarchy 1. Define IAM policies and access control 2. Plan shared networking and connectivity 3. Organize resources by project/folder/application