Question No. 1

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?

ACloud Infrastructure (Terraform) repository is shared: different directories are different environments
GKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different
overlay directories are different environments
Application (app source code) repositories are separated: different branches are different features

BCloud 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

CCloud Infrastructure (Terraform) repository is shared: different branches are different environments
GKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different
overlay directories are different environments
Application (app source code) repository is shared: different directories are different features

DCloud Infrastructure (Terraform) repositories are separated: different branches are different environments
GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:
different overlay directories are different environments
Application (app source code) repositories are separated: different branches are different features

Show Answer

Correct Answer: B

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 .

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

: GitOps-style continuous delivery with Cloud Build | Cloud Build Documentation | Google Cloud

Question No. 2

You are performing a semiannual capacity planning exercise for your flagship service. You expect a service user growth rate of 10% month-over-month over the next six months. Your service is fully containerized and runs on Google Cloud Platform (GCP). using a Google Kubernetes Engine (GKE) Standard regional cluster on three zones with cluster autoscaler enabled. You currently consume about 30% of your total deployed CPU capacity, and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth or as a result of zone failure, while avoiding unnecessary costs. How should you prepare to handle the predicted growth?

AVerity the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load test to verity your expected resource needs.

BBecause you are deployed on GKE and are using a cluster autoscaler. your GKE cluster will scale automatically, regardless of growth rate.

CBecause you are at only 30% utilization, you have significant headroom and you won't need to add any additional capacity for this rate of growth.

DProactively add 60% more node capacity to account for six months of 10% growth rate, and then perform a load test to make sure you have enough capacity.

Show Answer

Correct Answer: A

https://cloud.google.com/kubernetes-engine/docs/concepts/horizontalpodautoscaler

The Horizontal Pod Autoscaler changes the shape of your Kubernetes workload by automatically increasing or decreasing the number of Pods in response to the workload's CPU or memory consumption

Question No. 3

Your team of Infrastructure DevOps Engineers is growing, and you are starting to use Terraform to manage infrastructure. You need a way to implement code versioning and to share code with other team members. What should you do?

AStore the Terraform code in a version-control system. Establish procedures for pushing new versions and merging with the master.

BStore the Terraform code in a network shared folder with child folders for each version release. Ensure that everyone works on different files.

CStore the Terraform code in a Cloud Storage bucket using object versioning. Give access to the bucket to every team member so they can download the files.

DStore the Terraform code in a shared Google Drive folder so it syncs automatically to every team member's computer. Organize files with a naming convention that identifies each new version.

Show Answer

Correct Answer: A

https://www.terraform.io/docs/cloud/guides/recommended-practices/part3.3.html

Question No. 4

You support an application running on GCP and want to configure SMS notifications to your team for the most critical alerts in Stackdriver Monitoring. You have already identified the alerting policies you want to configure this for. What should you do?

ADownload and configure a third-party integration between Stackdriver Monitoring and an SMS gateway. Ensure that your team members add their SMS/phone numbers to the external tool.

BSelect the Webhook notifications option for each alerting policy, and configure it to use a third-party integration tool. Ensure that your team members add their SMS/phone numbers to the external tool.

CEnsure that your team members set their SMS/phone numbers in their Stackdriver Profile. Select the SMS notification option for each alerting policy and then select the appropriate SMS/phone numbers from the list.

DConfigure a Slack notification for each alerting policy. Set up a Slack-to-SMS integration to send SMS messages when Slack messages are received. Ensure that your team members add their SMS/phone numbers to the external integration.

Show Answer

Correct Answer: C

https://cloud.google.com/monitoring/support/notification-options#creating_channels

To configure SMS notifications, do the following:

In the SMS section, click Add new and follow the instructions. Click Save. When you set up your alerting policy, select the SMS notification type and choose a verified phone number from the list.

Question No. 5

As a Site Reliability Engineer, you support an application written in GO that runs on Google Kubernetes Engine (GKE) in production. After releasing a new version Of the application, you notice the application runs for about 15 minutes and then restarts. You decide to add Cloud Profiler to your application and now notice that the heap usage grows constantly until the application restarts. What should you do?

AAdd high memory compute nodes to the cluster.

BIncrease the memory limit in the application deployment.

CAdd Cloud Trace to the application, and redeploy.

DIncrease the CPU limit in the application deployment.

Show Answer

Correct Answer: B

The correct answer is B, Increase the memory limit in the application deployment.

The application is experiencing a memory leak, which means that it is allocating memory that is not freed or reused. This causes the heap usage to grow constantly until it reaches the memory limit of the pod, which triggers a restart by Kubernetes. Increasing the memory limit in the application deployment can help mitigate the problem by allowing the application to run longer before reaching the limit. However, this is not a permanent solution, as the memory leak will still occur and eventually exhaust the available memory. The best solution is to identify and fix the source of the memory leak in the application code, using tools like Cloud Profiler and pprof12.

Using Cloud Profiler with Go, Troubleshooting memory leaks. Profiling Go Programs, Heap profiles.

Free Google Professional-Cloud-DevOps-Engineer Exam Actual Questions

The questions for Professional-Cloud-DevOps-Engineer were last updated On Dec 17, 2024