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Google Professional-Cloud-DevOps-Engineer certification is a valuable credential for professionals seeking to advance their careers in DevOps and cloud computing. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification demonstrates the candidate's ability to deploy and manage applications using best practices and tools on the Google Cloud Platform, which is becoming increasingly popular among enterprises. Moreover, the certification is recognized globally, making it an excellent opportunity for professionals who want to work with global clients or organizations.

Google Professional-Cloud-DevOps-Engineer (Google Cloud Certified - Professional Cloud DevOps Engineer) Certification Exam is designed to test the technical skills and knowledge of professionals who work with Google Cloud Platform (GCP) and are responsible for designing, building, and managing highly scalable, highly available, and highly reliable cloud-native applications using DevOps principles and practices. Professional-Cloud-DevOps-Engineer Exam measures the candidate's ability to manage and automate workflows, deploy applications, monitor and optimize application performance, and implement security measures using GCP tools and services.

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Google Professional-Cloud-DevOps-Engineer Exam is designed to test the candidate's knowledge and skills related to DevOps engineering practices and Google Cloud technologies. Professional-Cloud-DevOps-Engineer exam comprises of two sections – the first section includes multiple-choice questions, while the second section includes practical scenarios that test the candidate's ability to apply their knowledge and skills to real-world situations.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q131-Q136):

NEW QUESTION # 131
Your company runs services by using multiple globally distributed Google Kubernetes Engine (GKE) clusters Your operations team has set up workload monitoring that uses Prometheus-based tooling for metrics alerts: and generating dashboards This setup does not provide a method to view metrics globally across all clusters You need to implement a scalable solution to support global Prometheus querying and minimize management overhead What should you do?

A. Configure Prometheus cross-service federation for centralized data access
B. Configure Prometheus hierarchical federation for centralized data access
C. Configure Google Cloud Managed Service for Prometheus
D. Configure workload metrics within Cloud Operations for GKE

Answer: C

Explanation:
The best option for implementing a scalable solution to support global Prometheus querying and minimize management overhead is to use Google Cloud Managed Service for Prometheus. Google Cloud Managed Service for Prometheus is a fully managed service that allows you to collect, query, and visualize metrics from your GKE clusters using Prometheus-based tooling. You can use Google Cloud Managed Service for Prometheus to query metrics across multiple clusters and regions using a global view. You can also use Google Cloud Managed Service for Prometheus to integrate with other Google Cloud services, such as Cloud Monitoring, Cloud Logging, and BigQuery. By using Google Cloud Managed Service for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.

NEW QUESTION # 132
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. Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
B. 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
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: A

NEW QUESTION # 133
You use a multiple step Cloud Build pipeline to build and deploy your application to Google Kubernetes Engine (GKE). You want to integrate with a third-party monitoring platform by performing a HTTP POST of the build information to a webhook. You want to minimize the development effort. What should you do?

A. Create a Cloud Pub/Sub push subscription to the Cloud Build cloud-builds PubSub topic to HTTP POST the build information to a webhook.
B. Use Stackdriver Logging to create a logs-based metric from the Cloud Build logs. Create an Alert with a Webhook notification type.
C. Add a new step at the end of the pipeline in Cloud Build to HTTP POST the build information to a webhook.
D. Add logic to each Cloud Build step to HTTP POST the build information to a webhook.

Answer: A

NEW QUESTION # 134
You support an application deployed on Compute Engine. The application connects to a Cloud SQL instance to store and retrieve data. After an update to the application, users report errors showing database timeout messages. The number of concurrent active users remained stable. You need to find the most probable cause of the database timeout. What should you do?

A. Check the serial port logs of the Compute Engine instance.
B. Use Cloud Security Scanner to see whether your Cloud SQL is under a Distributed Denial of Service (DDoS) attack.
C. Determine whether there is an increased number of connections to the Cloud SQL instance.
D. Use Stackdriver Profiler to visualize the resources utilization throughout the application.

Answer: C

Explanation:
Explanation
The most probable cause of the database timeout is an increased number of connections to the Cloud SQL instance. This could happen if the application does not close connections properly or if it creates too many connections at once. You can check the number of connections to the Cloud SQL instance using Cloud Monitoring or Cloud SQL Admin API .

NEW QUESTION # 135
Your team uses Cloud Build for all CI/CO pipelines. You want to use the kubectl builder for Cloud Build to deploy new images to Google Kubernetes Engine (GKE). You need to authenticate to GKE while minimizing development effort. What should you do?

A. Create a separate step in Cloud Build to retrieve service account credentials and pass these to kubectl.
B. Create a new service account with the Container Developer role and use it to run Cloud Build.
C. Specify the Container Developer role for Cloud Build in the cloudbuild.yaml file.
D. Assign the Container Developer role to the Cloud Build service account.

Answer: D

NEW QUESTION # 136
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