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Google Professional-Cloud-DevOps-Engineer certification exam is a highly sought-after certification that is designed for IT professionals who want to demonstrate their skills in managing and deploying applications on Google Cloud Platform (GCP). Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification validates the skills and knowledge of individuals in various areas of cloud computing, including DevOps practices, infrastructure automation, continuous delivery, and monitoring and logging.
Google Professional-Cloud-DevOps-Engineer Exam is designed to assess an individual's knowledge and skills in the field of cloud-based DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is intended for professionals who are interested in developing and deploying software applications in the cloud environment. Professional-Cloud-DevOps-Engineer exam is part of the Google Cloud Certified program, which is designed to validate the knowledge and expertise of professionals in various areas of cloud computing.
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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q92-Q97):
NEW QUESTION # 92
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. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.
- D. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.
Answer: D
NEW QUESTION # 93
You are running an experiment to see whether your users like a new feature of a web application. Shortly after deploying the feature as a canary release, you receive a spike in the number of 500 errors sent to users, and your monitoring reports show increased latency. You want to quickly minimize the negative impact on users. What should you do first?
- A. Trace the origin of 500 errors and the root cause of increased latency.
- B. Record data for the postmortem document of the incident.
- C. Roll back the experimental canary release.
- D. Start monitoring latency, traffic, errors, and saturation.
Answer: A
NEW QUESTION # 94
You work for a global organization and run a service with an availability target of 99% with limited engineering resources. For the current calendar month you noticed that the service has 99 5% availability. You must ensure that your service meets the defined availability goals and can react to business changes including the upcoming launch of new features You also need to reduce technical debt while minimizing operational costs You want to follow Google-recommended practices What should you do?
- A. Add N+1 redundancy to your service by adding additional compute resources to the service
- B. Allocate available engineers to the feature backlog while you ensure that the sen/ice remains within the availability target
- C. Identify, measure and eliminate toil by automating repetitive tasks
- D. Define an error budget for your service level availability and minimize the remaining error budget
Answer: C
NEW QUESTION # 95
You want to share a Cloud Monitoring custom dashboard with a partner team What should you do?
- A. Export the metrics to BigQuery Use Looker Studio to create a dashboard, and share the dashboard with the partner team
- B. Copy the Monitoring Query Language (MQL) query from the dashboard; and send the MQL query to the partner team
- C. Provide the partner team with the dashboard URL to enable the partner team to create a copy of the dashboard
- D. Download the JSON definition of the dashboard, and send the JSON file to the partner team
Answer: D
NEW QUESTION # 96
Your company has recently experienced several production service issues. You need to create a Cloud Monitoring dashboard to troubleshoot the issues, and you want to use the dashboard to distinguish between failures in your own service and those caused by a Google Cloud service that you use. What should you do?
- A. Create a logs widget to display system errors from Cloud Logging on the dashboard.
- B. Create a log-based metric to track cloud service errors, and display the metric on the dashboard.
- C. Enable Personalized Service Health annotations on the dashboard.
- D. Create an alerting policy for the system error metrics.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From General Cloud Monitoring Knowledge:
The key requirement is to distinguish between failures in your own service and those caused by an underlying Google Cloud service.
A: Enable Personalized Service Health annotations on the dashboard: Google Cloud Personalized Service Health provides information about incidents affecting Google Cloud services that may impact your projects.
When enabled and integrated with Monitoring, it can display these events as annotations on your dashboards, overlaying them on your service's metrics charts. This allows you to correlate dips in your service's performance with known Google Cloud service issues, directly addressing the need to distinguish failure origins.
B: Create an alerting policy for the system error metrics: Alerting policies are for notifications when metrics cross thresholds. While useful for detecting issues in your own service, they don't inherently distinguish the cause between your service and a Google Cloud dependency without further context, which option A provides.
C: Create a log-based metric to track cloud service errors, and display the metric on the dashboard: You could try to create log-based metrics from logs that might indicate a cloud service error (e.g., specific API error codes from Google Cloud services). However, this is indirect, might require complex parsing, and Personalized Service Health is a more direct and authoritative source for Google Cloud service disruptions.
D: Create a logs widget to display system errors from Cloud Logging on the dashboard: Similar to C, displaying raw system error logs can be helpful for troubleshooting your own service, but it doesn't provide a clear, curated view of whether a Google Cloud service itself is having an issue. It would require manual interpretation to link these logs to a potential Google Cloud outage.
Personalized Service Health is specifically designed to provide visibility into Google Cloud service incidents relevant to your resources. Integrating this with Monitoring dashboards is the most direct way to achieve the stated goal.
Reference (Based on Cloud Monitoring and Personalized Service Health features):
Personalized Service Health Overview: https://cloud.google.com/service-health/docs/overview Integrating with Cloud Monitoring: Documentation often shows how to enable annotations for Personalized Service Health events on Monitoring charts. This allows a visual correlation between your service metrics and Google Cloud service health events."Personalized Service Health integrates with Cloud Monitoring so you can see service health events alongside your metrics."
"You can enable annotations on your metric charts to display relevant Personalized Service Health events." This feature directly helps differentiate between issues in your application versus issues in the underlying Google Cloud services.
NEW QUESTION # 97
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