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Google Professional-Cloud-DevOps-Engineer 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Optimizing the service performance: It delves into the identification of service performance issues, implementation of debugging tools in Google Cloud, and optimization of resource utilization and costs.
トピック 2	Building and implementing CI CD pipelines for a service: Management and designing of CI CD pipelines, implementation of CI CD pipelines, management of CI CD configuration, and secrets are discussed in this paper. Moreover, it focuses on storage methods and key rotation services, and finally securing the CI CD deployment pipeline.
トピック 3	Applying site reliability engineering practices to a service: It deals with velocity, and reliability of the service, service lifecycle, healthy communication, and collaboration for operations. The topic also discusses mitigation of incident impact on users and conducting a postmortem.
トピック 4	Implementing service monitoring strategies: This topic covers structured and unstructured logs from Compute Engine, GKE, and serverless platforms using Cloud Logging. Its sub-topics include coverage of metrics with Cloud Monitoring, dashboards and alerts in Cloud Monitoring, Cloud Logging platform, and logging and monitoring access controls.
トピック 5	Bootstrapping a Google Cloud organization for DevOps: It discusses the overall resource hierarchy for an organization, infrastructure as code, CI CD architecture stack in Google Cloud, hybrid, and multi-cloud environments. Moreover, the topic focuses on multiple environments including staging and production.

>> Professional-Cloud-DevOps-Engineer過去問 <<

Professional-Cloud-DevOps-Engineer対応内容、Professional-Cloud-DevOps-Engineer日本語対策問題集

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam 認定 Professional-Cloud-DevOps-Engineer 試験問題 (Q105-Q110):

質問 # 105
You are deploying an application to Cloud Run. The application requires a password to start. Your organization requires that all passwords are rotated every 24 hours, and your application must have the latest password. You need to deploy the application with no downtime. What should you do?

A. Use Cloud Build to add your password into the application container at build time. Ensure that Artifact Registry is secured from public access.
B. Store the password in Secret Manager and send the secret to the application by using environment variables.
C. Store the password in Secret Manager and mount the secret as a volume within the application.
D. Store the password directly in the code. Use Cloud Build to rebuild and deploy the application each time the password changes.

正解：C

解説：
The correct answer is B. Store the password in Secret Manager and mount the secret as a volume within the application.
Secret Manager is a service that allows you to securely store and manage sensitive data such as passwords, API keys, certificates, and tokens.You can use Secret Manager to rotate your secrets automatically or manually, and access them from your Cloud Run applications1.
There are two ways to use secrets from Secret Manager in Cloud Run:
As environment variables: You can set environment variables that point to secrets in Secret Manager. Cloud Run will resolve the secrets at runtime and inject them into the environment of your application. However, this method has some limitations, such as:
The environment variables are cached for up to 10 minutes, so you may not get the latest version of the secret immediately.
The environment variables are visible in plain text in the Cloud Console and the Cloud SDK, which may expose sensitive information.
The environment variables are limited to 4 KB of data, which may not be enough for some secrets.2 As file system volumes: You can mount secrets from Secret Manager as files in a volume within your application. Cloud Run will create a tmpfs volume and write the secrets as files in it. This method has some advantages, such as:
The files are updated every 30 seconds, so you can get the latest version of the secret faster.
The files are not visible in the Cloud Console or the Cloud SDK, which provides better security.
The files can store up to 64 KB of data, which allows for larger secrets.3 Therefore, for your use case, it is better to use the second method and mount the secret as a file system volume within your application. This way, you can ensure that your application has the latest password, and you can deploy it with no downtime.
To mount a secret as a file system volume in Cloud Run, you can use the following command:
gcloud beta run deploy SERVICE --image IMAGE_URL --update-secrets=/path/to/file=secretName:version where:
SERVICE is the name of your Cloud Run service.
IMAGE_URL is the URL of your container image.
/path/to/file is the path where you want to mount the secret file in your application.
secretName is the name of your secret in Secret Manager.
version is the version of your secret.You can uselatestto get the most recent version.3 You can also use the Cloud Console to mount secrets as file system volumes. For more details, seeMounting secrets from Secret Manager.
References:
1:Overview | Secret Manager Documentation | Google Cloud
2:Using secrets as environment variables | Cloud Run Documentation | Google Cloud
3:Mounting secrets from Secret Manager | Cloud Run Documentation | Google Cloud

質問 # 106
Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

A. Configure the horizontal pod autoscaler to use the average response time from the Liveness and Readiness probes.
B. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.
C. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.
D. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.

正解：C

解説：
https://cloud.google.com/kubernetes-engine/docs/tutorials/autoscaling-metrics

質問 # 107
Your company runs applications in Google Kubernetes Engine (GKE) that are deployed following a GitOps methodology.
Application developers frequently create cloud resources to support their applications. You want to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices. You need to ensure that infrastructure as code reconciles periodically to avoid configuration drift. What should you do?

A. Install and configure Config Connector in Google Kubernetes Engine (GKE).
B. Configure Cloud Build with a Terraform builder to execute plan and apply commands.
C. Create a Pod resource with a Terraform docker image to execute terraform plan and terraform apply commands.
D. Create a Job resource with a Terraform docker image to execute terraforrm plan and terraform apply commands.

正解：A

質問 # 108
You need to run a business-critical workload on a fixed set of Compute Engine instances for several months. The workload is stable with the exact amount of resources allocated to it. You want to lower the costs for this workload without any performance implications. What should you do?

A. Create an Unmanaged Instance Group for the instances used to run the workload.
B. Purchase Committed Use Discounts.
C. Migrate the instances to a Managed Instance Group.
D. Convert the instances to preemptible virtual machines.

正解：A

質問 # 109
You are creating a CI/CD pipeline to perform Terraform deployments of Google Cloud resources Your CI/CD tooling is running in Google Kubernetes Engine (GKE) and uses an ephemeral Pod for each pipeline run You must ensure that the pipelines that run in the Pods have the appropriate Identity and Access Management (1AM) permissions to perform the Terraform deployments You want to follow Google-recommended practices for identity management What should you do?
Choose 2 answers

A. Create a new Google service account, and assign the appropriate 1AM permissions
B. Assign the appropriate 1AM permissions to the Google service account associated with the Compute Engine VM instances that run the Pods
C. Create a new JSON service account key for the Google service account store the key in the secret management store for the CI/CD tool and configure Terraform to use this key for authentication
D. Create a new JSON service account key for the Google service account store the key as a Kubernetes secret, inject the key into the Pods, and set the boogle_application_credentials environment variable
E. Create a new Kubernetes service account, and assign the service account to the Pods Use Workload Identity to authenticate as the Google service account

正解：A、E

解説：
Explanation
The best options for ensuring that the pipelines that run in the Pods have the appropriate IAM permissions to perform the Terraform deployments are to create a new Kubernetes service account and assign the service account to the Pods, and to use Workload Identity to authenticate as the Google service account. A Kubernetes service account is an identity that represents an application or a process running in a Pod. A Google service account is an identity that represents a Google Cloud resource or service. Workload Identity is a feature that allows you to bind Kubernetes service accounts to Google service accounts. By using Workload Identity, you can avoid creating and managing JSON service account keys, which are less secure and require more maintenance. You can also assign the appropriate IAM permissions to the Google service account that corresponds to the Kubernetes service account.

質問 # 110
......

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