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To earn the Google Professional-Cloud-DevOps-Engineer Certification, candidates must pass a rigorous exam that consists of multiple-choice and multiple-select questions. Professional-Cloud-DevOps-Engineer exam is designed to test the candidate's ability to apply their knowledge to real-world scenarios and use cases. Candidates must demonstrate their ability to design, develop, and deploy applications using GCP tools and services, as well as their ability to troubleshoot and optimize their applications for performance and scalability.

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Google Professional-Cloud-DevOps-Engineer Exam is a valuable credential for professionals who are looking to build a career in DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification validates the candidate's ability to design and manage continuous delivery systems and implement DevOps practices using Google Cloud technologies. To pass the exam, candidates should have a strong understanding of DevOps engineering practices and Google Cloud technologies, as well as hands-on experience with related tools and practices.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q190-Q195):

NEW QUESTION # 190
Your team is designing a new application for deployment both inside and outside Google Cloud Platform (GCP). You need to collect detailed metrics such as system resource utilization. You want to use centralized GCP services while minimizing the amount of work required to set up this collection system. What should you do?

A. Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.
B. Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.
C. Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.
D. Install an Application Performance Monitoring (APM) tool in both locations, and configure an export to a central data storage location for analysis.

Answer: D

NEW QUESTION # 191
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. Escalate the situation and request additional error budget.
B. 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.
C. Look through other metrics related to the product and find SLOs with remaining error budget. Reallocate the error budgets and allow the feature launch.
D. 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.

Answer: B

NEW QUESTION # 192
You are responding to a high-priority incident where a critical, user-facing payment service is experiencing a
50% error rate. The cause is a non-critical, batch analytics Dataflow pipeline flooding a shared Memorystore for Redis instance with writes, which has spiked read latency for the payment service. A full rollback of the Dataflow pipeline's deployment will take 15 minutes to complete through your CI/CD process. You need to restore the payment service as quickly as possible. What should you do?

A. In the Google Cloud console, edit the Memorystore for Redis instance and increase its capacity tier.
B. Initiate an automated rollback of the Dataflow pipeline's deployment to revert to the last stable version.
C. Cancel the active Dataflow job.
D. Use Cloud Profiler to inspect the Dataflow pipeline's execution graph to pinpoint the source of the excessive writes.

Answer: C

Explanation:
Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:
In incident management, the primary goal during the "Mitigation" phase is to restore service levels as quickly as possible, often referred to as "stopping the bleeding." Google Cloud's SRE principles emphasize that during a high-priority incident, you should prioritize actions that have an immediate impact over long-term fixes or deep-dive forensics. While a rollback (Option C) is a standard procedure, the 15-minute CI/CD overhead is too slow when a critical payment service is failing. Using Cloud Profiler (Option A) is a diagnostic step that should be reserved for the post-mortem phase, not the heat of the outage.
Canceling the active Dataflow job (Option D) is the fastest way to alleviate the pressure on the shared Memorystore instance. Since the Dataflow pipeline is "non-critical" and "batch-based," stopping it has a low business impact compared to the payment service outage. Once the job is canceled, the write pressure on Redis drops instantly, reducing read latency and restoring the payment service to health. This aligns with the SRE practice of "shedding load" to protect critical system components. Once the immediate crisis is resolved, the team can then investigate the root cause and implement long-term fixes like rate limiting or resource isolation.

NEW QUESTION # 193
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. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.
C. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.
D. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.

Answer: B

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.

NEW QUESTION # 194
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. Configure the build system with protected branches that require pull request approval.
C. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
D. Use an Admission Controller to verify that incoming requests originate from approved sources.

Answer: C

NEW QUESTION # 195
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