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Google Professional-Cloud-DevOps-Engineer (Google Cloud Certified - Professional Cloud DevOps Engineer) Exam is designed for professionals who have expertise in DevOps practices and technologies in the Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification validates the knowledge and skills required to design, develop, and implement DevOps processes and practices on Google Cloud Platform. Professional-Cloud-DevOps-Engineer Exam covers a wide range of topics including continuous integration and delivery, infrastructure as code, monitoring and logging, and site reliability engineering. It is ideal for DevOps engineers, site reliability engineers, automation engineers, and cloud architects who work with Google Cloud Platform.
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The Google Professional-Cloud-DevOps-Engineer exam is designed for professionals who have experience in cloud computing, DevOps, and software development. It covers a wide range of topics such as infrastructure automation, configuration management, continuous integration and delivery, and monitoring and logging. Professional-Cloud-DevOps-Engineer Exam also includes hands-on experience with Google Cloud Platform tools and services, such as Kubernetes, Cloud Build, and Cloud Monitoring.
NEW QUESTION # 194
You are developing a Node.js utility on a workstation in Cloud Workstations by using Code OSS. The utility is a simple web page, and you have already confirmed that all necessary firewall rules are in place. You tested the application by starting it on port 3000 on your workstation in Cloud Workstations, but you need to be able to access the web page from your local machine. You need to follow Google-recommended security practices.
What should you do?
Answer: A
Explanation:
Cloud Workstations provides a secure way to preview web applications using built-in preview links, which expose internal ports only via authenticated and authorized access - no public IP exposure required.
"To preview your app, click the preview icon in the Code OSS panel, which securely tunnels the service to your browser."
- Cloud Workstations - Previewing your app
This adheres to Google's security best practices by avoiding public IPs or manual tunneling while enabling developer efficiency.
NEW QUESTION # 195
You need to run a business-critical workload on a fixed set of Compute Engine instances for several months. The workload is stable with the exact amount of resources allocated to it. You want to lower the costs for this workload without any performance implications. What should you do?
Answer: C
NEW QUESTION # 196
You need to deploy a new service to production. The service needs to automatically scale using a Managed Instance Group (MIG) and should be deployed over multiple regions. The service needs a large number of resources for each instance and you need to plan for capacity. What should you do?
Answer: A
Explanation:
Explanation
https://cloud.google.com/compute/quotas#understanding_quotas
https://cloud.google.com/compute/quotas
NEW QUESTION # 197
Your team is running microservices in Google Kubernetes Engine (GKE) You want to detect consumption of an error budget to protect customers and define release policies What should you do?
Answer: B
Explanation:
Explanation
The best option for detecting consumption of an error budget to protect customers and define release policies is to create a service level objective (SLO) and create an alert policy on select_slo_burn_rate. A SLO is a target value or range of values for a service level indicator (SLI) that measures some aspect of the service quality, such as availability or latency. An error budget is the amount of time or number of errors that a service can tolerate while still meeting its SLO. A select_slo_burn_rate is a metric that indicates how fast the error budget is being consumed by the service. By creating an alert policy on select_slo_burn_rate, you can trigger notifications or actions when the error budget consumption exceeds a certain threshold. This way, you can balance change, velocity, and reliability of the service by adjusting the release policies based on the error budget status.
NEW QUESTION # 198
Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, and an application written in GO. You noticed that the performance inconsistently degrades at peak load. You could not reproduce this issue on your workstation. You need to continuously monitor the application in production to identify slow paths in the code. You want to minimize performance impact and management overhead.
What should you do?
Answer: B
Explanation:
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.
NEW QUESTION # 199
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