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

Section	Weight	Objectives
Topic 1: Optimizing performance and cost	17%	- Apply FinOps practices 1. Use spot instances and committed use discounts 2. Right-size resources and eliminate waste 3. Monitor and analyze cloud costs - Balance cost, performance, and reliability 1. Optimize over time 2. Make tradeoff decisions based on data 3. Plan capacity and scaling - Improve system performance 1. Tune applications and infrastructure 2. Use caching and content delivery 3. Optimize resource allocation
Topic 2: Implementing observability and troubleshooting	20%	- Troubleshoot complex issues 1. Trace requests end-to-end 2. Isolate problems across layers 3. Analyze performance and latency - Build monitoring and logging systems 1. Use Cloud Monitoring, Cloud Logging, Cloud Trace 2. Create dashboards and alerts 3. Collect and analyze metrics, logs, traces - Optimize observability strategy 1. Reduce noise and prioritize signals 2. Integrate observability into development 3. Scale monitoring for large environments
Topic 3: Applying site reliability engineering practices	18%	- Define reliability goals 1. Align reliability with business needs 2. Establish SLIs, SLOs, SLAs 3. Use error budgets to balance change and stability - Manage incidents and outages 1. Perform postmortems and root cause analysis 2. Detect and respond to issues quickly 3. Implement improvements to prevent recurrence - Design for resilience 1. Test failure scenarios and recovery 2. Plan redundancy and fault tolerance 3. Automate remediation and self-healing
Topic 4: Bootstrapping and maintaining a Google Cloud organization	20%	- Set up governance and compliance 1. Implement organization policies 2. Configure audit logging and monitoring 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. Plan shared networking and connectivity 2. Define IAM policies and access control 3. Organize resources by project/folder/application
Topic 5: Building and implementing CI/CD pipelines	25%	- Optimize and maintain pipelines 1. Monitor pipeline performance 2. Handle rollbacks and versioning 3. Improve speed and reliability - Design CI/CD pipelines 1. Support hybrid/multi-cloud deployment 2. Integrate artifact management 3. Plan pipelines for apps, infrastructure, ML workloads - Implement and secure pipelines 1. Manage pipeline secrets and credentials 2. Enforce security scanning and compliance checks 3. Automate build, test, and deployment stages

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam 認定 Professional-Cloud-DevOps-Engineer 試験問題 (Q66-Q71):

質問 # 66
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?

A. Because you are deployed on GKE and are using a cluster autoscaler. your GKE cluster will scale automatically, regardless of growth rate.
B. Because 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.
C. Proactively 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.
D. Verity the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load test to verity your expected resource needs.

正解：D

質問 # 67
Your team deploys applications to three Google Kubernetes Engine (GKE) environments development staging and production You use GitHub reposrtones as your source of truth You need to ensure that the three environments are consistent You want to follow Google-recommended practices to enforce and install network policies and a logging DaemonSet on all the GKE clusters in those environments What should you do?

A. Use Google Cloud Deploy to deploy the network policies and the DaemonSet Use Cloud Monitoring to trigger an alert if the network policies and DaemonSet drift from your source in the repository.
B. Use Cloud Build to render and deploy the network policies and the DaemonSet Set up a Policy Controller to enforce the configurations for the three environments
C. Use Cloud Build to render and deploy the network policies and the DaemonSet Set up Config Sync to sync the configurations for the three environments
D. Use Google Cloud Deploy to deploy the DaemonSet and use Policy Controller to configure the network policies Use Cloud Monitoring to detect drifts from the source in the repository and Cloud Functions to correct the drifts

正解：C

解説：
Explanation
The best option for ensuring that the three environments are consistent and following Google-recommended practices is to use Cloud Build to render and deploy the network policies and the DaemonSet, and set up Config Sync to sync the configurations for the three environments. Cloud Build is a service that executes your builds on Google Cloud infrastructure. You can use Cloud Build to render and deploy your network policies and DaemonSet as code using tools like Kustomize, Helm, or kpt. Config Sync is a feature that enables you to manage the configurations of your GKE clusters from a single source of truth, such as a Git repository. You can use Config Sync to sync the configurations for your development, staging, and production environments and ensure that they are consistent.

質問 # 68
You support a service with a well-defined Service Level Objective (SLO). Over the previous 6 months, your service has consistently met its SLO and customer satisfaction has been consistently high. Most of your service's operations tasks are automated and few repetitive tasks occur frequently. You want to optimize the balance between reliability and deployment velocity while following site reliability engineering best practices.
What should you do? (Choose two.)

A. Change the implementation of your Service Level Indicators (SLIs) to increase coverage.
B. Increase the service's deployment velocity and/or risk.
C. Shift engineering time to other services that need more reliability.
D. Make the service's SLO more strict.
E. Get the product team to prioritize reliability work over new features.

正解：B、C

解説：
(https://sre.google/workbook/implementing-slos/#slo-decision-matrix)

質問 # 69
You have an application that runs in Google Kubernetes Engine (GKE). The application consists of several microservices that are deployed to GKE by using Deployments and Services One of the microservices is experiencing an issue where a Pod returns 403 errors after the Pod has been running for more than five hours Your development team is working on a solution but the issue will not be resolved for a month You need to ensure continued operations until the microservice is fixed You want to follow Google-recommended practices and use the fewest number of steps What should you do?

A. Configure an alert to notify you whenever a Pod returns 403 errors
B. Monitor the Pods and terminate any Pods that have been running for more than five hours
C. Create a cron job to terminate any Pods that have been running for more than five hours
D. Add a HTTP liveness probe to the microservice s deployment

正解：D

解説：
Explanation
The best option for ensuring continued operations until the microservice is fixed is to add a HTTP liveness probe to the microservice's deployment. A HTTP liveness probe is a type of probe that checks if a Pod is alive by sending an HTTP request and expecting a success response code. If the probe fails, Kubernetes will restart the Pod. You can add a HTTP liveness probe to your microservice's deployment by using a livenessProbe field in your Pod spec. This way, you can ensure that any Pod that returns 403 errors after running for more than five hours will be restarted automatically and resume normal operations.

質問 # 70
You are building and deploying a microservice on Cloud Run for your organization Your service is used by many applications internally You are deploying a new release, and you need to test the new version extensively in the staging and production environments You must minimize user and developer impact. What should you do?

A. Deploy the new version of the service to the staging environment with a new-release tag without serving traffic Test the new-release version If the test passes; gradually roll out this tagged version Repeat for the production environment
B. Deploy the new version of the service to the staging environment Split the traffic, and allow 50% of traffic through to the latest version Test the latest version If the test passes, send all traffic to the latest version Repeat for the production environment
C. Deploy the new version of the service to the staging environment Split the traffic, and allow 1 % of traffic through to the latest version Test the latest version If the test passes gradually roll out the latest version to the staging and production environments
D. Deploy a new environment with the green tag to use as the staging environment Deploy the new version of the service to the green environment and test the new version If the tests pass, send all traffic to the green environment and delete the existing staging environment Repeat for the production environment

正解：A

解説：
Explanation
The best option for deploying a new release of your microservice on Cloud Run and testing it extensively in the staging and production environments with minimal user and developer impact is to deploy the new version of the service to the staging environment with a new-release tag without serving traffic, test the new-release version, and if the test passes, gradually roll out this tagged version. A tag is a label that you can assign to a revision of your service on Cloud Run. You can use tags to create different versions of your service without affecting traffic. You can also use tags to gradually roll out traffic to a new version of your service by using traffic splitting. This way, you can test your new release extensively in both environments and minimize user and developer impact.

質問 # 71
......

試験の結果は、Jpexam選択したProfessional-Cloud-DevOps-Engineer学習教材と直接関係しています。したがって、当社は試験のレビューに特に関心を持っています。試験の証明書を取得することはほんの始まりです。当社の練習資料は、広範囲に影響を与える可能性があります。この種の試験に関する要求は、Professional-Cloud-DevOps-EngineerトレーニングクイズでGoogle満たすことができます。ですから、私たちのGoogle Cloud Certified - Professional Cloud DevOps Engineer Exam練習資料はあなたの未来にプラスの興味を持っています。このような小さな投資でありながら大きな成功を収めたのに、Google Cloud Certified - Professional Cloud DevOps Engineer Examなぜあなたはまだためらっていますか？

Professional-Cloud-DevOps-Engineer日本語版トレーリング: https://www.jpexam.com/Professional-Cloud-DevOps-Engineer_exam.html

P.S.JpexamがGoogle Driveで共有している無料の2026 Google Professional-Cloud-DevOps-Engineerダンプ：https://drive.google.com/open?id=1pn9BJl6dQEwO-X0d0i3CVI8qCsuR2R2d