Professional-Cloud-DevOps-Engineer受験料過去問、Professional-Cloud-DevOps-Engineerリンクグローバル
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Professional-Cloud-DevOps-Engineer試験トレントの3つのバージョンを提供しており、PDFバージョン、PCバージョン、APPオンラインバージョンが含まれています。各バージョンの機能と使用方法は異なり、実際の状況に適した最も便利なバージョンを選択できます。たとえば、PDFバージョンは、Professional-Cloud-DevOps-Engineerテストトレントをダウンロードして印刷するのに便利で、学習の閲覧に適しています。 PDFバージョンを使用している場合は、ペーパーで急流Professional-Cloud-DevOps-Engineerガイドを印刷できます。 Professional-Cloud-DevOps-Engineer試験問題のPCバージョンは、Google Cloud Certified - Professional Cloud DevOps Engineer Exam実際の試験環境を刺激します。
今日、GoogleのProfessional-Cloud-DevOps-Engineer認定試験は、IT業界で多くの人に重視されています、それは、IT能力のある人の重要な基準の目安となっています。多くの人はGoogleのProfessional-Cloud-DevOps-Engineer試験への準備に悩んでいます。この記事を読んだあなたはラッキーだと思います。あなたは最高の方法を探しましたから。私たちの強力なPass4Testチームの開発するGoogleのProfessional-Cloud-DevOps-Engineerソフトを使用して試験に保障があります。まだ躊躇?最初に私たちのソフトウェアのデモを無料でダウンロードしよう。
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Professional-Cloud-DevOps-Engineerリンクグローバル、Professional-Cloud-DevOps-Engineer問題サンプル
今日の社会では、能力を高めるために証明書を取得することを優先する人がますます増えています。 Googleまったく新しい観点から、Pass4TestのProfessional-Cloud-DevOps-Engineer学習資料は、Professional-Cloud-DevOps-Engineer認定の取得を目指すほとんどのオフィスワーカーに役立つように設計されています。 当社のProfessional-Cloud-DevOps-Engineerテストガイドは、現代の人材開発に歩調を合わせ、すべての学習者を社会のニーズに適合させます。 Google Cloud Certified - Professional Cloud DevOps Engineer Examの最新の質問が、関連する知識の蓄積と能力強化のための最初の選択肢になることは間違いありません。
Google Cloud Certified - Professional Cloud DevOps Engineer Exam 認定 Professional-Cloud-DevOps-Engineer 試験問題 (Q10-Q15):
質問 # 10
You use Terraform to manage an application deployed to a Google Cloud environment The application runs on instances deployed by a managed instance group The Terraform code is deployed by using aCI/CD pipeline When you change the machine type on the instance template used by the managed instance group, the pipeline fails at the terraform apply stage with the following error message
You need to update the instance template and minimize disruption to the application and the number of pipeline runs What should you do?
- A. Add a new instance template update the managed instance group to use the new instance template and delete the old instance template
- B. Remove the managed instance group from the Terraform state file update the instance template and reimport the managed instance group.
- C. Delete the managed instance group and recreate it after updating the instance template
- D. Set the create_bef ore_destroy meta-argument to true in the lifecycle block on the instance template
正解:D
解説:
The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.
質問 # 11
You currently store the virtual machine (VM) utilization logs in Stackdriver. You need to provide an easy-to-share interactive VM utilization dashboard that is updated in real time and contains information aggregated on a quarterly basis. You want to use Google Cloud Platform solutions. What should you do?
- A. 1. Export VM utilization logs from Stackdriver to BigOuery.
2. Create a dashboard in Data Studio.
3. Share the dashboard with your stakeholders. - B. 1. Export VM utilization logs (rom Stackdriver to BigQuery.
2. From BigQuery. export the logs to a CSV file.
3. Import the CSV file into Google Sheets.
4. Build a dashboard in Google Sheets and share it with your stakeholders. - C. 1. Export VM utilization logs from Stackdriver to a Cloud Storage bucket.
2. Enable the Cloud Storage API to pull the logs programmatically.
3. Build a custom data visualization application.
4. Display the pulled logs in a custom dashboard. - D. 1. Export VM utilization logs from Stackdriver to Cloud Pub/Sub.
2. From Cloud Pub/Sub, send the logs to a Security Information and Event Management (SIEM) system.
3. Build the dashboards in the SIEM system and share with your stakeholders.
正解:B
質問 # 12
You are configuring your CI/CD pipeline natively on Google Cloud. You want builds in a pre-production Google Kubernetes Engine (GKE) environment to be automatically load-tested before being promoted to the production GKE environment. You need to ensure that only builds that have passed this test are deployed to production. You want to follow Google-recommended practices. How should you configure this pipeline with Binary Authorization?
- A. Create an attestation for the builds that pass the load test by requiring the lead quality assurance engineer to sign the attestation by using their personal private key.
- B. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.
- C. Create an attestation for the builds that pass the load test by requiring the lead quality assurance engineer to sign the attestation by using a key stored in Cloud Key Management Service (Cloud KMS).
- D. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) with a service account JSON key stored as a Kubernetes Secret.
正解:B
解説:
The correct answer is B. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.
According to the Google Cloud documentation, Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run1.
Binary Authorization uses attestations to certify that a specific image has completed a previous stage in the CI
/CD pipeline, such as passing a load test2.Attestations are signed by private keys that are associated with attestors, which are entities that verify the attestations3.To follow Google-recommended practices, you should store your private keys in Cloud Key ManagementService (Cloud KMS), which is a secure and scalable service for managing cryptographic keys4.You should also use Workload Identity, which is a feature that allows Kubernetes service accounts to act as Google service accounts, to authenticate to Cloud KMS and sign attestations without having to manage or expose service account keys5.
The other options are incorrect because they do not follow Google-recommended practices. Option A and option D require human intervention to sign the attestations, which is not scalable or automated. Option C exposes the service account JSON key as a Kubernetes Secret, which is less secure than using Workload Identity.
Reference:
Creating an attestor, Creating an attestor.Cloud Key Management Service Documentation, Overview.
Attestations overview, Attestations overview.Using Workload Identity with Binary Authorization, Using Workload Identity with Binary Authorization.Binary Authorization, Binary Authorization.
質問 # 13
As part of your company's initiative to shift left on security, the infoSec team is asking all teams to implement guard rails on all the Google Kubernetes Engine (GKE) clusters to only allow the deployment of trusted and approved images You need to determine how to satisfy the InfoSec teams goal of shifting left on security. What should you do?
- A. Enable Container Analysis in Artifact Registry, and check for common vulnerabilities and exposures (CVEs) in your container images
- B. Use Binary Authorization to attest images during your CI CD pipeline
- C. Deploy Falco or Twistlock on GKE to monitor for vulnerabilities on your running Pods
- D. Configure Identity and Access Management (1AM) policies to create a least privilege model on your GKE clusters
正解:B
質問 # 14
You recently noticed that one Of your services has exceeded the error budget for the current rolling window period. Your company's product team is about to launch a new feature. You want to follow Site Reliability Engineering (SRE) practices.
What should you do?
- A. Look through other metrics related to the product and find SLOs with remaining error budget.
Reallocate the error budgets and allow the feature launch. - B. Notify the team about the lack of error budget and ensure that all their tests are successful so the launch will not further risk the error budget.
- C. Escalate the situation and request additional error budget.
- D. Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.
正解:D
解説:
The correct answer is A. Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.
According to the Site Reliability Engineering (SRE) practices, an error budget is the amount of unreliability that a service can tolerate without harming user satisfaction1.An error budget is derived from the service-level objectives (SLOs), which are the measurable goals for the service quality2. When a service exceeds its error budget, it means that it has violated its SLOs and may have negatively impacted the users.In this case, the SRE team should notify the product team that their error budget is used up and negotiate with them for a launch freeze or a lower SLO3. A launch freeze means that no new features are deployed until the service reliability is restored. A lower SLO means that the product team accepts a slightly worse user experience in exchange for launching new features. Both options require a trade-off between reliability and innovation, and should be agreed upon by both teams.
The other options are incorrect because they do not follow the SRE practices.Option B is incorrect because it violates the principle of error budget autonomy, which means that each service should have its own error budget and SLOs, and should not borrow or reallocate them from other services4. Option C is incorrect because it does not address the root cause of the error budget overspend, and may create unrealistic expectations for the service reliability. Option D is incorrect because it does not prevent the possibility of introducing new errors or bugs with the feature launch, which may further degrade the service quality and user satisfaction.
質問 # 15
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