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Google Professional Cloud DevOps Engineer Exam Questions
Free Google Professional Cloud DevOps Engineer Exam Actual Questions
Note: Premium Questions for Professional Cloud DevOps Engineer were last updated On Aug. 25, 2026 (see below)
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?
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.
Your company runs an ecommerce website built with JVM-based applications and microservice architecture in Google Kubernetes Engine (GKE) The application load increases during the day and decreases during the night Your operations team has configured the application to run enough Pods to handle the evening peak load You want to automate scaling by only running enough Pods and nodes for the load What should you do?
The best option for automating scaling by only running enough Pods and nodes for the load is to configure the Horizontal Pod Autoscaler and enable the cluster autoscaler. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. The cluster autoscaler is a feature that automatically adjusts the size of a node pool based on the demand for node capacity. By using both features together, you can ensure that your application runs enough Pods to handle the load, and that your cluster runs enough nodes to host the Pods. This way, you can optimize your resource utilization and cost efficiency.
You are managing an application that exposes an HTTP endpoint without using a load balancer. The latency of the HTTP responses is important for the user experience. You want to understand what HTTP latencies all of your users are experiencing. You use Stackdriver Monitoring. What should you do?
https://sre.google/workbook/implementing-slos/
https://cloud.google.com/architecture/adopting-slos/
Latency is commonly measured as a distribution. Given a distribution, you can measure various percentiles. For example, you might measure the number of requests that are slower than the historical 99th percentile.
You work for a global organization and run a service with an availability target of 99% with limited engineering resources. For the current calendar month you noticed that the service has 99 5% availability. You must ensure that your service meets the defined availability goals and can react to business changes including the upcoming launch of new features You also need to reduce technical debt while minimizing operational costs You want to follow Google-recommended practices What should you do?
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_datasetYou need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?
The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export. Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.
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