更新する-ハイパスレートのProfessional-Cloud-DevOps-Engineer技術試験試験-試験の準備方法Professional-Cloud-DevOps-Engineer合格内容

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Google Professional-Cloud-DevOps-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Certified - Professional Cloud DevOps Engineer Exam
Exam Number:	Professional-Cloud-DevOps-Engineer
Available Languages:	English, Japanese
Exam Duration:	120 minutes
Passing Score:	Not publicly disclosed
Exam Format:	Multiple select, Multiple choice
Exam Price:	$200 (plus tax where applicable)
Certificate Validity Period:	2 years
Real Exam Qty:	50-60
Related Certifications:	Professional Cloud Network Engineer Professional Cloud Developer Professional Cloud Security Engineer Professional Cloud Architect
Recommended Training:	Exam Guide Professional Cloud DevOps Engineer Learning Path
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Cloud-DevOps-Engineer Sample Questions
Exam Way:	Online proctored (remote) or onsite proctored (testing center)
Pre Condition:	No mandatory prerequisites; recommended: 3+ years industry experience, 1+ year managing production systems on Google Cloud
Official Syllabus URL:	https://cloud.google.com/learn/certification/cloud-devops-engineer

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Professional-Cloud-DevOps-Engineer合格内容 & Professional-Cloud-DevOps-Engineer日本語版

Professional-Cloud-DevOps-Engineer証明書は、クライアントの知識と実用能力を向上させる実用性と役割のため、多数の証明書の中でも際立っています。テストProfessional-Cloud-DevOps-Engineer証明書を所有することは、クライアントが仕事を見つけ、クライアントが有能な人々であることの証拠を見つけるときに重いコーリングカードを所有することと同じです。 Professional-Cloud-DevOps-Engineerクイズ準備は、クライアントがテストの準備をするのに最適なオプションです。 Professional-Cloud-DevOps-Engineer学習資料は、高い合格率とヒット率を高めます。クライアントは、それらを使用した後に高く評価し、Professional-Cloud-DevOps-Engineer認定に合格するための重要なツールとして認識します。

Google Professional-Cloud-Devops-Enginer認定を獲得することで、雇用主に、DevOpsのプラクティスでクラウドテクノロジーを効果的に管理するために必要なスキルと知識があることを雇用主に実証できます。この認定は、クラウドの専門家にとってより良い雇用機会とより高い給与にもつながる可能性があります。

Google Cloud Certified - Professional Cloud DevOps Engineer Exam 認定 Professional-Cloud-DevOps-Engineer 試験問題 (Q86-Q91):

質問 # 86
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 applicationruns 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?

A. Add Cloud Trace to the application, and redeploy.
B. Increase the memory limit in the application deployment.
C. Add high memory compute nodes to the cluster.
D. Increase the CPU limit in the application deployment.

正解：B

解説：
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.

質問 # 87
Your organization is running multiple Google Kubernetes Engine (GKE) clusters in a project. You need to design a highly-available solution to collect and query both domain-specific workload metrics and GKE default metrics across all clusters, while minimizing operational overhead. What should you do?

A. Enable managed collection on every GKE cluster. Query the metrics in Cloud Monitoring.
B. Use Prometheus Operator to install Prometheus in every cluster and scrape the metrics. Ensure that a Thanos sidecar is enabled on every Prometheus instance. Configure Thanos in the central cluster. Query the central Thanos instance.
C. Use Prometheus Operator to install Prometheus in every cluster and scrape the metrics. Configure remote-write to one central Prometheus. Query the central Prometheus instance.
D. Enable managed collection on every GKE cluster. Query the metrics in BigQuery.

正解：A

解説：
Google recommends using managed collection for GKE monitoring to reduce operational overhead. This sends Kubernetes metrics and custom workload metrics directly to Cloud Monitoring.
"You can enable Managed Service for Prometheus, which provides a fully managed Prometheus-compatible monitoring solution integrated with Cloud Monitoring."
- Managed Collection in GKE
"It automatically collects and stores metrics in Cloud Monitoring with no need to manage a Prometheus server."
- Google Cloud Managed Prometheus
It's scalable, cost-effective, and requires no additional infrastructure like Thanos or Prometheus federation.

質問 # 88
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. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
B. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
C. Configure the build system with protected branches that require pull request approval.
D. Use an Admission Controller to verify that incoming requests originate from approved sources.

正解：B

解説：
Explanation
The keywords here is "developers or operators". Option A the operators could push images to production without approval (operators could touch the cluster directly and the cluster cannot do any action against them).
Rest same as francisco_guerra.

質問 # 89
You are investigating issues in your production application that runs on Google Kubernetes Engine (GKE).
You determined that the source Of the issue is a recently updated container image, although the exact change in code was not identified. The deployment is currently pointing to the latest tag. You need to update your cluster to run a version of the container that functions as intended. What should you do?

A. Build a new container from a previous Git tag, and do a rolling update on the deployment to the new container.
B. Alter the deployment to point to the sha2 56 digest of the previously working container.
C. Create a new tag called stable that points to the previously working container, and change the deployment to point to the new tag.
D. Apply the latest tag to the previous container image, and do a rolling update on the deployment.

正解：B

質問 # 90
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. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.
C. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.
D. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.

正解：D

解説：
Explanation
https://cloud.google.com/kubernetes-engine/docs/tutorials/autoscaling-metrics The Google Cloud HTTP Load Balancer (GCLB) provides metrics on the number of requests and the response latency for each backend service. These metrics can be used as custom metrics for the horizontal pod autoscaler (HPA) to scale the deployment based on the load. This is the correct solution to use an appropriate SLI for scaling.

質問 # 91
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