Professional-Cloud-DevOps-Engineer Exam Dumps : Google Cloud Certified - Professional Cloud DevOps Engineer Exam

To deploy only on new release tags, which follow a semantic versioning pattern (e.g., v1.2.3), you should use a tag filter with a regular expression matching semantic versions.

From the official documentation:

"You can configure Cloud Build to start a build automatically whenever there is a new tag in your source repository by using tag triggers."

— Cloud Build Triggers

Using a tag pattern like \d+\.\d+\.\d* ensures that builds only trigger on properly versioned release tags (minimizing unnecessary executions). This matches standard semver tags such as 1.0.0, 2.1.3, etc.

Cloud Build Private Pools allow your builds to run in a secure, isolated environment with direct access to resources inside your private VPC network, without exposing them to the public internet. This is the Google-recommended approach for minimizing management overhead while maintaining strong security boundaries.

From the official documentation:

"Private pools run your builds in a dedicated and secure environment that can connect to private network resources."

— Cloud Build Private Pools Overview

"You can configure private pools to access resources in a VPC network, which enables secure access to services without using public IPs."

— Accessing Private Resources

This method eliminates the need to create and manage additional compute instances or complex load balancing setups, providing seamless integration with your private services during CI pipeline execution.

The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.

According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1. Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.

The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.