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Real Professional-Cloud-DevOps-Engineer Exam - Test Professional-Cloud-DevOps-Engineer Testking

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Google Professional-Cloud-DevOps-Engineer Certification is intended for individuals who possess a strong background in software development, operations, and deployment. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification program is designed to help professionals gain a deeper understanding of cloud computing concepts, DevOps principles, and best practices for deploying and maintaining applications on GCP.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q173-Q178):

NEW QUESTION # 173
Your organization has a containerized web application that runs on-premises As part of the migration plan to Google Cloud you need to select a deployment strategy and platform that meets the following acceptance criteria
1 The platform must be able to direct traffic from Android devices to an Android-specific microservice
2 The platform must allow for arbitrary percentage-based traffic splitting
3 The deployment strategy must allow for continuous testing of multiple versions of any microservice What should you do?

A. Deploy the canary release of the application to Compute Engine Use Anthos Service Mesh with Compute Engine to direct 10% of user traffic to the canary release by configuring the virtual service.
B. Deploy the canary release of the application to Cloud Run Use traffic splitting to direct 10% of user traffic to the canary release based on the revision tag
C. Deploy the canary release to Google Kubernetes Engine with Anthos Sen/ice Mesh Use traffic splitting to direct 10% of user traffic to the new version based on the user-agent header configured in the virtual service
D. Deploy the canary release of the application to App Engine Use traffic splitting to direct a subset of user traffic to the new version based on the IP address

Answer: C

NEW QUESTION # 174
You support a web application that runs on App Engine and uses CloudSQL and Cloud Storage for data storage. After a short spike in website traffic, you notice a big increase in latency for all user requests, increase in CPU use, and the number of processes running the application. Initial troubleshooting reveals:
After the initial spike in traffic, load levels returned to normal but users still experience high latency.
Requests for content from the CloudSQL database and images from Cloud Storage show the same high latency.
No changes were made to the website around the time the latency increased.
There is no increase in the number of errors to the users.
You expect another spike in website traffic in the coming days and want to make sure users don't experience latency. What should you do?

A. Move the application from App Engine to Compute Engine.
B. Modify the App Engine configuration to have additional idle instances.
C. Upgrade the GCS buckets to Multi-Regional.
D. Enable high availability on the CloudSQL instances.

Answer: B

Explanation:
Scaling App Engine scales the number of instances automatically in response to processing volume. This scaling factors in the automatic_scaling settings that are provided on a per-version basis in the configuration file. A service with basic scaling is configured by setting the maximum number of instances in the max_instances parameter of the basic_scaling setting. The number of live instances scales with the processing volume. You configure the number of instances of each version in that service's configuration file. The number of instances usually corresponds to the size of a dataset being held in memory or the desired throughput for offline work. You can adjust the number of instances of a manually-scaled version very quickly, without stopping instances that are currently running, using the Modules API set_num_instances function. https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed
https://cloud.google.com/appengine/docs/standard/python/config/appref
max_idle_instances Optional. The maximum number of idle instances that App Engine should maintain for this version. Specify a value from 1 to 1000. If not specified, the default value is automatic, which means App Engine will manage the number of idle instances. Keep the following in mind: A high maximum reduces the number of idle instances more gradually when load levels return to normal after a spike. This helps your application maintain steady performance through fluctuations in request load, but also raises the number of idle instances (and consequent running costs) during such periods of heavy load.

NEW QUESTION # 175
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 workload metrics within Cloud Operations for GKE
C. Configure Prometheus hierarchical federation for centralized data access
D. Configure Google Cloud Managed Service for Prometheus

Answer: D

Explanation:
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 # 176
You are running an application in a virtual machine (VM) using a custom Debian image. The image has the Stackdriver Logging agent installed. The VM has the cloud-platform scope. The application is logging information via syslog. You want to use Stackdriver Logging in the Google Cloud Platform Console to visualize the logs. You notice that syslog is not showing up in the "All logs" dropdown list of the Logs Viewer. What is the first thing you should do?

A. Look for the agent's test log entry in the Logs Viewer.
B. SSH to the VM and execute the following commands on your VM: ps ax I grep fluentd
C. Verify the VM service account access scope includes the monitoring.write scope.
D. Install the most recent version of the Stackdriver agent.

Answer: B

Explanation:
https://cloud.google.com/compute/docs/access/service-
accounts#associating_a_service_account_to_an_instance

NEW QUESTION # 177
You support a large service with a well-defined Service Level Objective (SLO). The development team deploys new releases of the service multiple times a week. If a major incident causes the service to miss its SLO, you want the development team to shift its focus from working on features to improving service reliability. What should you do before a major incident occurs?

A. Add a plugin to your Jenkins pipeline that prevents new releases whenever your service is out of SLO.
B. Develop an appropriate error budget policy in cooperation with all service stakeholders.
C. Negotiate with the product team to always prioritize service reliability over releasing new features.
D. Negotiate with the development team to reduce the release frequency to no more than once a week.

Answer: B

Explanation:
Explanation
Reason : Incident has not occurred yet, even when development team is already pushing new features multiple times a week. The option A says, to define an error budget "policy", not to define error budget(It is already present). Just simple means to bring in all stakeholders, and decide how to consume the error budget effectively that could bring balance between feature deployment and reliability.
The goals of this policy are to: -- Protect customers from repeated SLO misses -- Provide an incentive to balance reliability with other features https://sre.google/workbook/error-budget-policy/

NEW QUESTION # 178
......

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