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

Section	Weight	Objectives
Monitoring, Logging, and Debugging	12%	- Log management 1. Implementing log-based metrics 2. Structuring logs for efficient querying 3. Configuring log retention policies - Cloud Operations Suite 1. Creating uptime checks and alerts 2. Configuring Cloud Monitoring metrics 3. Implementing Cloud Logging with log sinks - Debugging and troubleshooting 1. Debugging with Cloud Trace distributed tracing 2. Analyzing performance profiles with Cloud Profiler 3. Using Cloud Debugger and Error Reporting
CI/CD Pipeline Development	26%	- CI/CD best practices 1. Implementing shift-left testing 2. Managing secrets in CI/CD pipelines 3. Configuring quality gates and code coverage - Designing and implementing CI/CD pipelines 1. Configuring build triggers and webhooks 2. Implementing build automation scripts 3. Configuring build caching strategies 4. Setting up artifact management with Artifact Registry 5. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.) - Containerization and Docker 1. Creating optimized Docker images (multi-stage builds) 2. Managing container registries 3. Implementing container security best practices
Reliability and Site Reliability Engineering (SRE)	24%	- SLOs, SLIs, and SLAs 1. Error budget policies and management 2. Defining and implementing Service Level Objectives 3. Setting appropriate Service Level Agreements 4. Creating and interpreting Service Level Indicators - Incident management 1. Implementing on-call procedures 2. Configuring automated incident response 3. Post-incident reviews and blameless postmortems - Monitoring and observability 1. Implementing distributed tracing with Cloud Trace 2. Configuring Cloud Monitoring and Logging 3. Creating dashboards for service health visibility 4. Setting up alerting policies and incident management
Microservices Architecture	14%	- Container orchestration with GKE 1. Managing pod lifecycle and resource quotas 2. Configuring node pools and auto-scaling 3. Implementing workload deployment and scaling 4. Designing Kubernetes cluster architectures - API management 1. Implementing API versioning strategies 2. Exposing APIs with Cloud Endpoints / Apigee - Service mesh and networking 1. Configuring traffic management and load balancing 2. Implementing service-to-service authentication 3. Implementing Anthos Service Mesh / Istio
Cloud Infrastructure Automation	24%	- Deployment strategies 1. Rolling updates with Kubernetes 2. Canary releases and progressive rollouts 3. Blue-green deployments 4. Feature flags with Firebase Remote Config or LaunchDarkly - Configuration management 1. Managing secrets with Secret Manager 2. Using Ansible for configuration management 3. Implementing configuration drift detection - Infrastructure as Code (IaC) 1. Implementing with Terraform 2. Implementing immutable infrastructure patterns 3. Managing infrastructure modules and state

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
You support the backend of a mobile phone game that runs on a Google Kubernetes Engine (GKE) cluster. The application is serving HTTP requests from users. You need to implement a solution that will reduce the network cost. What should you do?

A. Configure a Google Cloud HTTP Load Balancer as Ingress.
B. Configure your Kubernetes duster as a Private Cluster.
C. Configure the VPC as a Shared VPC Host project.
D. Configure your network services on the Standard Tier.

Answer: A

Explanation:
Costs associated with a load balancer are charged to the project containing the load balancer components. Because of these benefits, container-native load balancing is the recommended solution for load balancing through Ingress. When NEGs are used with GKE Ingress, the Ingress controller facilitates the creation of all aspects of the L7 load balancer. This includes creating the virtual IP address, forwarding rules, health checks, firewall rules, and more. https://cloud.google.com/architecture/best-practices-for-running-cost-effective-kubernetes-applications-on-gke

NEW QUESTION # 77
Your company follows Site Reliability Engineering practices. You are the person in charge of Communications for a large, ongoing incident affecting your customer-facing applications. There is still no estimated time for a resolution of the outage. You are receiving emails from internal stakeholders who want updates on the outage, as well as emails from customers who want to know what is happening. You want to efficiently provide updates to everyone affected by the outage. What should you do?

A. Delegate the responding to internal stakeholder emails to another member of the Incident Response Team. Focus on providing responses directly to customers.
B. Provide all internal stakeholder emails to the Incident Commander, and allow them to manage internal communications. Focus on providing responses directly to customers.
C. Focus on responding to internal stakeholders at least every 30 minutes. Commit to "next update" times.
D. Provide periodic updates to all stakeholders in a timely manner. Commit to a "next update" time in all communications.

Answer: C

NEW QUESTION # 78
Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, and an application written in GO. You noticed that the performance inconsistently degrades at peak load. You could not reproduce this issue on your workstation. You need to continuously monitor the application in production to identify slow paths in the code. You want to minimize performance impact and management overhead.
What should you do?

A. Periodically run the go tool pprof command against the application instance. Analyze the results by using flame graphs.
B. Use Cloud Monitoring to assess the App Engine CPU utilization metric.
C. Install a continuous profiling tool into Compute Engine. Configure the application to send profiling data to the tool.
D. Configure Cloud Profiler, and initialize the cloud.go@gle.com/go/profiler library in the application.

Answer: D

Explanation:
The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.
According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1.
Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.
The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.

NEW QUESTION # 79
You are using Terraform to manage infrastructure as code within a Cl/CD pipeline You notice that multiple copies of the entire infrastructure stack exist in your Google Cloud project, and a new copy is created each time a change to the existing infrastructure is made You need to optimize your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time. You want to follow Google-recommended practices What should you do?

A. Create a new pipeline to delete old infrastructure stacks when they are no longer needed
B. Verify that the pipeline is storing and retrieving the terrafom.tfstat* file from a source control
C. Update the pipeline to remove any existing infrastructure before you apply the latest configuration
D. Confirm that the pipeline is storing and retrieving the terraform. if state file from Cloud Storage with the Terraform gcs backend

Answer: D

Explanation:
Explanation
The best option for optimizing your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time is to confirm that the pipeline is storing and retrieving the terraform.tfstate file from Cloud Storage with the Terraform gcs backend. The terraform.tfstate file is a file that Terraform uses to store the current state of your infrastructure. The Terraform gcs backend is a backend type that allows you to store the terraform.tfstate file in a Cloud Storage bucket. By using the Terraform gcs backend, you can ensure that your pipeline has access to the latest state of your infrastructure and avoid creating multiple copies of the entire infrastructure stack.

NEW QUESTION # 80
Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to a Kubernetes cluster in the production environment. The security auditor is concerned that developers or operators could circumvent automated testing and push code changes to production without approval. What should you do to enforce approvals?

A. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
B. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
C. Use an Admission Controller to verify that incoming requests originate from approved sources.
D. Configure the build system with protected branches that require pull request approval.

Answer: A

NEW QUESTION # 81
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