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Google Professional-Cloud-DevOps-Engineer Certification Exam is designed for professionals who want to demonstrate their proficiency in deploying and managing applications on Google Cloud Platform (GCP). Professional-Cloud-DevOps-Engineer exam measures an individual's ability to design, build, and manage continuous delivery pipelines for software development, configure and manage infrastructure, and monitor and manage operations on GCP. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is ideal for DevOps engineers, software developers, and operations professionals who have experience with GCP and want to validate their skills and knowledge.
Earning the Google Professional-Cloud-DevOps-Engineer Certification can demonstrate to employers that an individual has the skills and knowledge needed to effectively manage cloud technologies with DevOps practices. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification can also lead to better job opportunities and higher salaries for cloud professionals.
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Google Cloud Certified Professional Cloud DevOps Engineer exam is designed to test the skills and knowledge of professionals who can help organizations improve their software delivery pipelines and build reliable, scalable, and secure applications on Google Cloud Platform (GCP). Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification verifies that the candidate possesses the necessary skills to design, implement, and manage DevOps processes using GCP tools and services.
NEW QUESTION # 22
Your company follows Site Reliability Engineering practices. You are the Incident Commander for a new. customer-impacting incident. You need to immediately assign two incident management roles to assist you in an effective incident response. What roles should you assign?
Choose 2 answers
Answer: A,D
Explanation:
https://sre.google/workbook/incident-response/
"The main roles in incident response are the Incident Commander (IC), Communications Lead (CL), and Operations or Ops Lead (OL)." The Operations Lead is responsible for managing the operational aspects of the incident, such as deploying fixes, rolling back changes, or restoring backups. The External Customer Communications Lead is not a standard role in incident response, but it could be delegated by the Communications Lead if needed.
NEW QUESTION # 23
You are designing a system with three different environments: development, quality assurance (QA), and production.
Each environment will be deployed with Terraform and has a Google Kubemetes Engine (GKE) cluster created so that application teams can deploy their applications. Anthos Config Management will be used and templated to deploy infrastructure level resources in each GKE cluster. All users (for example, infrastructure operators and application owners) will use GitOps. How should you structure your source control repositories for both Infrastructure as Code (laC) and application code?
Answer: C
Explanation:
The correct answer is B, Cloud Infrastructure (Terraform) repository is shared: different directories are different environments. GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different branches are different environments. Application (app source code) repositories are separated: different branches are different features.
This answer follows the best practices for using Terraform and Anthos Config Management with GitOps, as described in the following sources:
For Terraform, it is recommended to use a single repository for all environments, and use directories to separate them. This way, you can reuse the same Terraform modules and configurations across environments, and avoid code duplication and drift. You can also use Terraform workspaces to isolate the state files for each environment12.
For Anthos Config Management, it is recommended to use separate repositories for each environment, and use branches to separate the clusters within each environment. This way, you can enforce different policies and configurations for each environment, and use pull requests to promote changes across environments. You can also use Kustomize to create overlays for each cluster that apply specific patches or customizations34.
For application code, it is recommended to use separate repositories for each application, and use branches to separate the features or bug fixes for each application. This way, you can isolate the development and testing of each application, and use pull requests to merge changes into the main branch. You can also use tags or labels to trigger deployments to different environments5 .
Reference:
1: Best practices for using Terraform | Google Cloud
2: Terraform Recommended Practices - Part 1 | Terraform - HashiCorp Learn
3: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
4: Using Kustomize with Anthos Config Management | Anthos Config Management Documentation | Google Cloud
5: Deploy Anthos on GKE with Terraform part 3: Continuous Delivery with Cloud Build | Google Cloud Blog
6: GitOps-style continuous delivery with Cloud Build | Cloud Build Documentation | Google Cloud
NEW QUESTION # 24
You are monitoring a service that uses n2-standard-2 Compute Engine instances that serve large files. Users have reported that downloads are slow. Your Cloud Monitoring dashboard shows that your VMS are running at peak network throughput. You want to improve the network throughput performance. What should you do?
Answer: D
Explanation:
Explanation
The correct answer is C. Change the machine type for your VMs to n2-standard-8.
According to the Google Cloud documentation, the network throughput performance of a Compute Engine VM depends on its machine type1. The n2-standard-2 machine type has a maximum egress bandwidth of 4 Gbps, which can be a bottleneck for serving large files. By changing the machine type to n2-standard-8, you can increase the maximum egress bandwidth to 16 Gbps, which can improve the network throughput performance and reduce the download time for users. You also need to enable per VM Tier_1 networking performance, which is a feature that allows VMs to achieve higher network performance than the default settings2.
The other options are incorrect because they do not improve the network throughput performance of your VMs. Option A is incorrect because Cloud NAT is a service that allows private IP addresses to access the internet, but it does not increase the network bandwidth or speed3. Option B is incorrect because adding additional network interfaces (NICs) or IP addresses per NIC does not increase ingress or egress bandwidth for a VM1. Option D is incorrect because deploying the Ops Agent can help you monitor and troubleshoot your VMs, but it does not affect the network throughput performance4.
NEW QUESTION # 25
Your team is preparing to launch a new API in Cloud Run. The API uses an OpenTelemetry agent to send distributed tracing data to Cloud Trace to monitor the time each request takes. The team has noticed inconsistent trace collection. You need to resolve the issue. What should you do?
Answer: D
Explanation:
Comprehensive and Detailed Explanation:
Cloud Run scales down to zero when idle. If CPU is allocated only during request processing, background telemetry tasks (like OpenTelemetry traces) may not complete before the container shuts down.
Solution: Configure CPU to be always-allocated # This ensures that background tasks (like trace exports) continue even when the service is idle.
#Why not other options?
A (Increasing CPU to 4 cores)## More CPU won't help if traces are being cut off due to shutdown.
B (Using an HTTP health check)## Doesn't affect OpenTelemetry traces.
C (CPU only during request processing)## This causes the problem because traces need CPU after request completion to be exported.
#Official Reference:
Cloud Run Tracing with OpenTelemetry
Cloud Run Always-Allocated CPU
NEW QUESTION # 26
You manage an application that runs in Google Kubernetes Engine (GKE) and uses the blue/green deployment methodology Extracts of the Kubernetes manifests are shown below:
The Deployment app-green was updated to use the new version of the application During post-deployment monitoring you notice that the majority of user requests are failing You did not observe this behavior in the testing environment You need to mitigate the incident impact on users and enable the developers to troubleshoot the issue What should you do?
Answer: B
Explanation:
The best option for mitigating the incident impact on users and enabling the developers to troubleshoot the issue is to change the selector on the Service app-svc to app: my-app, version: blue. A Service is a resource that defines how to access a set of Pods. A selector is a field that specifies which Pods are selected by the Service. By changing the selector on the Service app-svc to app: my-app, version: blue, you can ensure that the Service only routes traffic to the Pods that have both labels app: my-app and version: blue. These Pods belong to the Deployment app-blue, which uses the previous version of the application. This way, you can mitigate the incident impact on users by switching back to the working version of the application. You can also enable the developers to troubleshoot the issue with the new version of the application in the Deployment app-green without affecting users.
NEW QUESTION # 27
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