Google marks4sure cloud Devops Engineer Professional-cloud-devops-engineer Book by Frances q24 vce pdf

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Exam Name:	Google Cloud Certified - Professional Cloud DevOps Engineer Exam
Exam Code:	Professional-Cloud-DevOps-Engineer Dumps
Vendor:	Google	Certification:	Cloud DevOps Engineer
Questions:	201 Q&A's	Shared By:	frances

Question 16

Your company runs services by using Google Kubernetes Engine (GKE). The GKE clusters in the development environment run applications with verbose logging enabled. Developers view logs by using the kubect1 logs

command and do not use Cloud Logging. Applications do not have a uniform logging structure defined. You need to minimize the costs associated with application logging while still collecting GKE operational logs. What should you do?

Options:

Run the gcloud container clusters update --logging—SYSTEM command for the development cluster.

Run the gcloud container clusters update logging=WORKLOAD command for the development cluster.

Run the gcloud logging sinks update _Defau1t --disabled command in the project associated with the development environment.

Add the severity >= DEBUG resource. type "k83 container" exclusion filter to the Default logging sink in the project associated with the development environment.

Discussion

Question 17

You are responsible for creating and modifying the Terraform templates that define your Infrastructure. Because two new engineers will also be working on the same code, you need to define a process and adopt a tool that will prevent you from overwriting each other's code. You also want to ensure that you capture all updates in the latest version. What should you do?

Options:

• Store your code in a Git-based version control system.• Establish a process that allows developers to merge their own changes at the end of each day.• Package and upload code lo a versioned Cloud Storage bucket as the latest master version.

• Store your code in a Git-based version control system.• Establish a process that includes code reviews by peers and unit testing to ensure integrity and functionality before integration of code.• Establish a process where the fully integrated code in the repository becomes the latest master version.

• Store your code as text files in Google Drive in a defined folder structure that organizes the files.• At the end of each day. confirm that all changes have been captured in the files within the folder structure.• Rename the folder structure with a predefined naming convention that increments the version.

• Store your code as text files in Google Drive in a defined folder structure that organizes the files.• At the end of each day, confirm that all changes have been captured in the files within the folder structure and create a new .zip archive with a predefined naming convention.• Upload the .zip archive to a versioned Cloud Storage bucket and accept it as the latest version.

Discussion

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Julian Oct 13, 2026

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Inaaya

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Fraser Oct 11, 2026

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Question 18

You are responsible for the reliability of a high-volume enterprise application. A large number of users report that an important subset of the application’s functionality – a data intensive reporting feature – is consistently failing with an HTTP 500 error. When you investigate your application’s dashboards, you notice a strong correlation between the failures and a metric that represents the size of an internal queue used for generating reports. You trace the failures to a reporting backend that is experiencing high I/O wait times. You quickly fix the issue by resizing the backend’s persistent disk (PD). How you need to create an availability Service Level Indicator (SLI) for the report generation feature. How would you define it?

Options:

As the I/O wait times aggregated across all report generation backends

As the proportion of report generation requests that result in a successful response

As the application’s report generation queue size compared to a known-good threshold

As the reporting backend PD throughout capacity compared to a known-good threshold

Discussion

Question 19

You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline. Your application has an in-memory cache that loads objects at start time. You want to automate the comparison of the canary version against the production version. How should you configure the canary analysis?

Options:

Compare the canary with a new deployment of the current production version.

Compare the canary with a new deployment of the previous production version.

Compare the canary with the existing deployment of the current production version.

Compare the canary with the average performance of a sliding window of previous production versions.