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To be eligible for the Professional-Cloud-DevOps-Engineer certification exam, candidates need to have at least three years of experience in cloud computing, software development, and DevOps practices. They also need to have a good understanding of GCP services and tools, including Compute Engine, Kubernetes Engine, Cloud Storage, Cloud SQL, Cloud Build, and Cloud Monitoring. Candidates are recommended to take the GCP Associate Cloud Engineer and GCP Professional Cloud Architect certification exams before attempting the Professional-Cloud-DevOps-Engineer Certification Exam.

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Google Professional-Cloud-DevOps-Engineer Certification is a highly esteemed certification program for professionals seeking to validate their expertise in cloud-based DevOps engineering. It is designed to test the knowledge and skills required to deploy, manage, and monitor secure, scalable, and highly available cloud-based infrastructure and applications.

The Professional-Cloud-DevOps-Engineer exam covers various topics, including cloud architecture and infrastructure, continuous delivery and release management, site reliability engineering, security, compliance, and troubleshooting. Candidates who pass the exam demonstrate their ability to design, implement, and manage scalable and secure solutions using Google Cloud technologies. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is suitable for professionals who work in DevOps roles, cloud architects, and those who are responsible for developing and maintaining cloud-based applications and services.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
You support the backend of a mobile phone game that runs on a Google Kubernetes Engine (GKE) cluster.
The application is serving HTTP requests from users. You need to implement a solution that will reduce the network cost. What should you do?

A. Configure a Google Cloud HTTP Load Balancer as Ingress.
B. Configure your network services on the Standard Tier.
C. Configure the VPC as a Shared VPC Host project.
D. Configure your Kubernetes duster as a Private Cluster.

Answer: B

Explanation:
Explanation
The Standard Tier network service offers lower network costs than the Premium Tier. This is the correct option to reduce the network cost for the application3.

NEW QUESTION # 73
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 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.
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 BigOuery.
2. Create a dashboard in Data Studio.
3. Share the dashboard with your stakeholders.

Answer: B

NEW QUESTION # 74
You are designing a continuous delivery (CD) strategy for a new serverless application. The application is packaged as a container image, stored in Artifact Registry, and deployed to Cloud Run. Your design requires a staging environment, a fully-managed Google Cloud service, mandatory manual approval for production deployments, and a phased rollout to production. Your solution should minimize administrative overhead.
What should you do?

A. Use Cloud Deploy to define a single delivery pipeline that promotes a release between a staging target and a production target. Configure the production target to require approval and to automatically execute a phased rollout that incrementally shifts traffic.
B. Define two separate Cloud Deploy pipelines. Configure the first pipeline to deploy to staging, and configure the second pipeline to trigger and execute a phased, canary rollout to the production Cloud Run service.
C. Use a Cloud Build trigger to initiate a GitOps workflow. Configure the trigger to update a manifest in a Git repository, which a controller on a GKE Autopilot cluster then synchronizes to manage a phased traffic rollout to the new revision.
D. Use Cloud Build to create a multi-stage pipeline. Configure the trigger to require approval before starting the build. Use the deploy command with the --traffic flag to incrementally shift traffic to the new revision in production.

Answer: A

Explanation:
Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:
Google Cloud Deploy is the platform's premier, fully-managed continuous delivery service specifically designed to simplify the progression of software through a series of environments (targets). For a Cloud Run application, Cloud Deploy provides a native, low-overhead way to define a single delivery pipeline that encapsulates both the staging and production targets. This approach directly satisfies the requirement for a fully-managed service while minimizing administrative toil compared to custom-scripted Cloud Build workflows (Option C) or complex GitOps controllers on GKE (Option B).
Key features of Cloud Deploy relevant here are its built-in support for mandatory manual approvals and automated canary/phased rollouts. By defining these in the delivery-pipeline YAML, you can ensure that a release is only promoted to production after a designated user approves it in the console or via CLI. Once approved, Cloud Deploy manages the traffic shifting to the new Cloud Run revision incrementally according to your specified percentages. This eliminates the need for managing multiple pipelines (Option D) and leverages native platform capabilities to ensure a reliable, repeatable, and secure deployment process that adheres to Google Cloud's modern DevOps and SRE deployment standards.

NEW QUESTION # 75
You encounter a large number of outages in the production systems you support. You receive alerts for all the outages that wake you up at night. The alerts are due to unhealthy systems that are automatically restarted within a minute. You want to set up a process that would prevent staff burnout while following Site Reliability Engineering practices. What should you do?

A. Eliminate unactionable alerts.
B. Create an incident report for each of the alerts.
C. Redefine the related Service Level Objective so that the error budget is not exhausted.
D. Distribute the alerts to engineers in different time zones.

Answer: A

NEW QUESTION # 76
Your organization is using Helm to package containerized applications Your applications reference both public and private charts Your security team flagged that using a public Helm repository as a dependency is a risk You want to manage all charts uniformly, with native access control and VPC Service Controls What should you do?

A. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend
B. Store public and private charts in OCI format by using Artifact Registry
C. Store public and private charts by using GitHub Enterprise with Google Workspace as the identity provider
D. Store public and private charts by using Git repository Configure Cloud Build to synchronize contents of the repository into a Cloud Storage bucket Connect Helm to the bucket by using https: // [bucket]
.srorage.googleapis.com/ [holnchart] as the Helm repository

Answer: B

Explanation:
Explanation
The best option for managing all charts uniformly, with native access control and VPC Service Controls is to store public and private charts in OCI format by using Artifact Registry. Artifact Registry is a service that allows you to store and manage container images and other artifacts in Google Cloud. Artifact Registry supports OCI format, which is an open standard for storing container images and other artifacts such as Helm charts. You can use Artifact Registry to store public and private charts in OCI format and manage them uniformly. You can also use Artifact Registry's native access control features, such as IAM policies and VPC Service Controls, to secure your charts and control who can access them.

NEW QUESTION # 77
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