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New Professional-Cloud-DevOps-Engineer Dumps Pdf | Actual Professional-Cloud-DevOps-Engineer Test

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Google Professional-Cloud-DevOps-Engineer Certification Exam is an essential certification for professionals who want to validate their skills and expertise in Cloud DevOps engineering. It demonstrates to potential employers that the candidate has the necessary knowledge and experience to design, deploy, and manage applications on the Google Cloud platform. If you're a DevOps engineer looking to take your career to the next level, this certification is definitely worth considering.

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Google Professional-Cloud-DevOps-Engineer Exam is a challenging exam that requires candidates to have a strong understanding of DevOps engineering practices and Google Cloud technologies. To prepare for the exam, candidates should have a solid understanding of the topics covered in the exam, such as continuous delivery, automation, infrastructure as code, and monitoring. They should also have hands-on experience with Google Cloud technologies and related DevOps tools and practices.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_dataset You need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?

A. Create two aggregated log sinks at the organization level, and filter by project ID
B. Create a log sink in each project
C. Create an aggregated Iog sink in the Dev and Prod folders
D. Create a single aggregated log sink at the organization level.

Answer: C

Explanation:
Explanation
The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export.
Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.

NEW QUESTION # 35
You are ready to deploy a new feature of a web-based application to production. You want to use Google Kubernetes Engine (GKE) to perform a phased rollout to half of the web server pods.
What should you do?

A. Use a partitioned rolling update.
B. Use a replica set in the deployment specification.
C. Use a stateful set with parallel pod management policy.
D. Use Node taints with NoExecute.

Answer: A

NEW QUESTION # 36
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 the VPC as a Shared VPC Host project.
B. Configure a Google Cloud HTTP Load Balancer as Ingress.
C. Configure your network services on the Standard Tier.
D. Configure your Kubernetes duster as a Private Cluster.

Answer: C

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 # 37
You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens over time. What should you do?

A. Configure BigOuery as a sink for Stackdriver Logging. Create a scheduled query to filter the cache miss logs and write them to a separate table
B. Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
C. Configure Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs.
D. Link Stackdriver Logging as a source in Google Data Studio. Filler (he logs on the cache misses.

Answer: B

Explanation:
https://cloud.google.com/logging/docs/logs-based-metrics#counter-metric

NEW QUESTION # 38
Your team is designing a new application for deployment into Google Kubernetes Engine (GKE). You need to set up monitoring to collect and aggregate various application-level metrics in a centralized location. You want to use Google Cloud Platform services while minimizing the amount of work required to set up monitoring.
What should you do?

A. Install the Cloud Pub/Sub client libraries, push various metrics from the application to various topics, and then observe the aggregated metrics in Stackdriver.
B. Publish various metrics from the application directly to the Slackdriver Monitoring API, and then observe these custom metrics in Stackdriver.
C. Install the OpenTelemetry client libraries in the application, configure Stackdriver as the export destination for the metrics, and then observe the application's metrics in Stackdriver.
D. Emit all metrics in the form of application-specific log messages, pass these messages from the containers to the Stackdriver logging collector, and then observe metrics in Stackdriver.

Answer: B

Explanation:
Explanation
https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics#custom_metrics
https://github.com/GoogleCloudPlatform/k8s-stackdriver/blob/master/custom-metrics-stackdriver-adapter/READ Your application can report a custom metric to Cloud Monitoring. You can configure Kubernetes to respond to these metrics and scale your workload automatically. For example, you can scale your application based on metrics such as queries per second, writes per second, network performance, latency when communicating with a different application, or other metrics that make sense for your workload.
https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics

NEW QUESTION # 39
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