Updated Nov-2025 Exam Professional-Cloud-DevOps-Engineer Dumps - Pass Your Certification Exam
Latest Real Google Professional-Cloud-DevOps-Engineer Exam Dumps Questions
To be eligible to take the Professional-Cloud-DevOps-Engineer certification exam, candidates must have at least three years of experience in software development, DevOps, or system administration. They must also have experience using Google Cloud Platform services and be proficient in at least one programming language.
NEW QUESTION # 84
Your organization stores all application logs from multiple Google Cloud projects in a central Cloud Logging project. Your security team wants to enforce a rule that each project team can only view their respective logs, and only the operations team can view all the logs. You need to design a solution that meets the security team's requirements, while minimizing costs. What should you do?
- A. Export logs to BigQuery tables for each project team. Grant project teams access to their tables. Grant logs writer access to the operations team in the central logging project.
- B. Create Identity and Access Management (IAM) roles for each project team and restrict access to the _ Default log view in their individual Google Cloud project. Grant viewer access to the operations team in the central logging project.
- C. Create log views for each project team, and only show each project team their application logs. Grant the operations team access to the _ Al Il-jogs View in the central logging project.
- D. Grant each project team access to the project _ Default view in the central logging project. Grant logging viewer access to the operations team in the central logging project.
Answer: C
Explanation:
Explanation
Create log views for each project team, and only show each project team their application logs. Grant the operations team access to the _AllLogs View in the central logging project1.
This approach aligns with the Google Cloud's recommended methodologies for Professional Cloud DevOps Engineers1. Log views allow you to create and manage access control at a finer granularity for your logs. By creating a separate log view for each project team, you can ensure that they only have access to their respective logs. The operations team, on the other hand, can be granted access to the _AllLogs view in the central logging project, allowing them to view all logs as required.
This solution not only meets the security team's requirements but also minimizes costs as it leverages built-in features of Google Cloud's logging and does not require exporting logs to another service like BigQuery (as suggested in option A), which could incur additional costs1.
NEW QUESTION # 85
You recently noticed that one of your services has exceeded the error budget for the current rolling window period. Your company's product team is about to launch a new feature. You want to follow Site Reliability Engineering (SRE) practices. What should you do?
- A. Escalate the situation and request additional error budget.
- B. Look through other metrics related to the product and find SLOs with remaining error budget. Reallocate the error budgets and allow the feature launch.
- C. Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.
- D. Notify the team about the lack of error budget and ensure that all their tests are successful so the launch will not further risk the error budget.
Answer: C
NEW QUESTION # 86
You support a high-traffic web application that runs on Google Cloud Platform (GCP). You need to measure application reliability from a user perspective without making any engineering changes to it. What should you do?
Choose 2 answers
- A. Modify the code to capture additional information for user interaction.
- B. Review current application metrics and add new ones as needed.
- C. Create new synthetic clients to simulate a user journey using the application.
- D. Analyze the web proxy logs only and capture response time of each request.
- E. Use current and historic Request Logs to trace customer interaction with the application.
Answer: A,D
NEW QUESTION # 87
You are developing the deployment and testing strategies for your CI/CD pipeline in Google Cloud You must be able to
* Reduce the complexity of release deployments and minimize the duration of deployment rollbacks
* Test real production traffic with a gradual increase in the number of affected users You want to select a deployment and testing strategy that meets your requirements What should you do?
- A. Recreate deployment and canary testing
- B. Rolling update deployment and A/B testing
- C. Blue/green deployment and canary testing
- D. Rolling update deployment and shadow testing
Answer: C
Explanation:
The best option for selecting a deployment and testing strategy that meets your requirements is to use blue/green deployment and canary testing. A blue/green deployment is a deployment strategy that involves creating two identical environments, one running the current version of the application (blue) and one running the new version of the application (green). The traffic is switched from blue to green after testing the new version, and if any issues are discovered, the traffic can be switched back to blue instantly. This way, you can reduce the complexity of release deployments and minimize the duration of deployment rollbacks. A canary testing is a testing strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test real production traffic with a gradual increase in the number of affected users.
NEW QUESTION # 88
Your development team has created a new version of their service's API. You need to deploy the new versions of the API with the least disruption to third-party developers and end users of third-party installed applications. What should you do?
- A. Introduce the new version of the API.
Announce deprecation of the old version of the API.
Deprecate the old version of the API.
Contact remaining users of the old API.
Provide best effort support to users of the old API.
Turn down the old version of the API. - B. Announce deprecation of the old version of the API.
Contact remaining users on the old API.
Introduce the new version of the API.
Deprecate the old version of the API.
Provide best effort support to users of the old API.
Turn down the old version of the API. - C. Announce deprecation of the old version of the API.
Introduce the new version of the API.
Contact remaining users on the old API.
Deprecate the old version of the API.
Turn down the old version of the API.
Provide best effort support to users of the old API. - D. Introduce the new version of the API.
Contact remaining users of the old API.
Announce deprecation of the old version of the API.
Deprecate the old version of the API.
Turn down the old version of the API.
Provide best effort support to users of the old API.
Answer: A
NEW QUESTION # 89
Your company runs applications in Google Kubernetes Engine (GKE). Several applications rely on ephemeral volumes. You noticed some applications were unstable due to the DiskPressure node condition on the worker nodes. You need to identify which Pods are causing the issue, but you do not have execute access to workloads and nodes. What should you do?
- A. Locate all the Pods with emptyDir volumes. Use the du -sh * command to measure volume disk usage.
- B. Check the metric by using Metrics Explorer.
- C. Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.
- D. Locate all the Pods with emptyDir volumes. use the df-h command to measure volume disk usage.
Answer: C
NEW QUESTION # 90
You need to enforce several constraint templates across your Google Kubernetes Engine (GKE) clusters. The constraints include policy parameters, such as restricting the Kubernetes API. You must ensure that the policy parameters are stored in a GitHub repository and automatically applied when changes occur. What should you do?
- A. When there is a change in GitHub, use a webhook to send a request to Cloud Service Mesh, and apply the change.
- B. Configure Config Connector with the GitHub repository. When there is a change in the repository, use Config Connector to apply the change.
- C. Set up a GitHub action to trigger Cloud Build when there is a parameter change. In Cloud Build, run a gcloud CLI command to apply the change.
- D. Configure Config Sync with the GitHub repository. When there is a change in the repository, use Config Sync to apply the change.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From General GKE and GitOps Knowledge:
The requirements are:
Enforce constraint templates (implying a policy agent like OPA Gatekeeper) on GKE.
Store policy parameters in a GitHub repository.
Automatically apply changes from the GitHub repository to the clusters.
This is a classic GitOps scenario.
A: Set up a GitHub action to trigger Cloud Build when there is a parameter change. In Cloud Build, run a gcloud CLI command to apply the change.This is a plausible CI/CD approach. GitHub Actions can trigger Cloud Build, which can then use kubectl or gcloud to apply configurations. However, this is a push-based imperative approach. GitOps tools offer a more declarative, pull-based model specifically designed for syncing Kubernetes configurations.
B: When there is a change in GitHub, use a webhook to send a request to Cloud Service Mesh, and apply the change.Cloud Service Mesh (based on Istio) is primarily for managing traffic, security, and observability for microservices. It's not designed for applying general Kubernetes policy configurations like Gatekeeper constraints from a Git repository.
C: Configure Config Sync with the GitHub repository. When there is a change in the repository, use Config Sync to apply the change.Config Sync is a Google Cloud product specifically designed for GitOps with GKE (and other Kubernetes clusters). It synchronizes configurations (including CustomResourceDefinitions for constraint templates and the constraints themselves) from a Git repository (like GitHub) to your clusters. It continuously monitors the repository and automatically applies any committed changes to the clusters, ensuring they remain in the desired state. This perfectly matches the requirements.
D: Configure Config Connector with the GitHub repository. When there is a change in the repository, use Config Connector to apply the change.Config Connector allows you to manage Google Cloud resources (like Pub/Sub topics, Spanner instances, etc.) using Kubernetes-style declarative configurations and kubectl. While it uses Kubernetes tooling, its primary purpose is managing Google Cloud resources, not syncing general Kubernetes configurations like Gatekeeper constraints from Git. Config Sync is the tool for syncing arbitrary Kubernetes manifests from Git to a cluster.
Config Sync is the Google Cloud tool built for the exact purpose described: maintaining consistency between Kubernetes cluster configurations and a Git repository using a GitOps model.
Reference (Based on Google Cloud GKE and Config Sync documentation):
Config Sync Overview: https://cloud.google.com/anthos-config-management/docs/config-sync-overview or
https://cloud.google.com/kubernetes-engine/docs/add-on/config-sync/overview (if referring to it as a GKE add-on)."Config Sync is a GitOps tool that helps you keep your Google Kubernetes Engine (GKE) Enterprise edition clusters synchronized with configs stored in a Git repository." It supports syncing various Kubernetes objects, including CustomResources, which are used by OPA Gatekeeper for defining constraints and constraint templates.
It automatically pulls changes from the Git repository and applies them, which meets the "automatically applied when changes occur" requirement.
NEW QUESTION # 91
You are configuring a CI pipeline. The build step for your CI pipeline integration testing requires access to APIs inside your private VPC network. Your security team requires that you do not expose API traffic publicly. You need to implement a solution that minimizes management overhead. What should you do?
- A. Use Cloud Build private pools to connect to the private VPC.
- B. Use Cloud Build to create a Compute Engine instance in the private VPC. Run the integration tests on the VM by using a startup script.
- C. Use Cloud Build as a pipeline runner. Configure a global external Application Load Balancer with a Google Cloud Armor policy for API access.
- D. Use Cloud Build as a pipeline runner. Configure a cross-region internal Application Load Balancer for API access.
Answer: A
Explanation:
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.
NEW QUESTION # 92
You support a high-traffic web application with a microservice architecture. The home page of the application displays multiple widgets containing content such as the current weather, stock prices, and news headlines. The main serving thread makes a call to a dedicated microservice for each widget and then lays out the homepage for the user. The microservices occasionally fail; when that happens, the serving thread serves the homepage with some missing content. Users of the application are unhappy if this degraded mode occurs too frequently, but they would rather have some content served instead of no content at all. You want to set a Service Level Objective (SLO) to ensure that the user experience does not degrade too much. What Service Level Indicator {SLI) should you use to measure this?
- A. A latency SLI: the ratio of microservice calls that complete in under 100 ms to the total number of microservice calls
- B. An availability SLI: the ratio of healthy microservices to the total number of microservices
- C. A freshness SLI: the proportion of widgets that have been updated within the last 10 minutes
- D. A quality SLI: the ratio of non-degraded responses to total responses
Answer: B
NEW QUESTION # 93
You are configuring Cloud Logging for a new application that runs on a Compute Engine instance with a public IP address. A user-managed service account is attached to the instance. You confirmed that the necessary agents are running on the instance but you cannot see any log entries from the instance in Cloud Logging. You want to resolve the issue by following Google-recommended practices. What should you do?
- A. Export the service account key and configure the agents to use the key.
- B. Update the instance to use the default Compute Engine service account.
- C. Enable Private Google Access on the subnet that the instance is in.
- D. Add the Logs Writer role to the service account.
Answer: D
Explanation:
The correct answer is A. Add the Logs Writer role to the service account.
To use Cloud Logging, the service account attached to the Compute Engine instance must have the necessary permissions to write log entries. The Logs Writer role (roles/logging.logWriter) provides this permission.You can grant this role to the user-managed service account at the project, folder, or organization level1.
Private Google Access is not required for Cloud Logging, as it allows instances without external IP addresses to access Google APIs and services2.The default Compute Engine service account already has the Logs Writer role, but it is not a recommended practice to use it for user applications3.Exporting the service account key and configuring the agents to use the key is not a secure way of authenticating the service account, as it exposes the key to potential compromise4.
References:
1:Access control with IAM | Cloud Logging | Google Cloud
2: Private Google Access overview | VPC | Google Cloud
3: Service accounts | Compute Engine Documentation | Google Cloud
4: Best practices for securing service accounts | IAM Documentation | Google Cloud
NEW QUESTION # 94
You want to share a Cloud Monitoring custom dashboard with a partner team What should you do?
- A. Copy the Monitoring Query Language (MQL) query from the dashboard; and send the MQL query to the partner team
- B. Download the JSON definition of the dashboard, and send the JSON file to the partner team
- C. Provide the partner team with the dashboard URL to enable the partner team to create a copy of the dashboard
- D. Export the metrics to BigQuery Use Looker Studio to create a dashboard, and share the dashboard with the partner team
Answer: B
Explanation:
The best option for sharing a Cloud Monitoring custom dashboard with a partner team is to provide the partner team with the dashboard URL to enable the partner team to create a copy of the dashboard. A Cloud Monitoring custom dashboard is a dashboard that allows you to create and customize charts and widgets to display metrics, logs, and traces from your Google Cloud resources and applications. You can share a custom dashboard with a partner team by providing them with the dashboard URL, which is a link that allows them to view the dashboard in their browser. The partner team can then create a copy of the dashboard in their own project by using the Copy Dashboard option. This way, they can access and modify the dashboard without affecting the original one.
NEW QUESTION # 95
You are analyzing Java applications in production. All applications have Cloud Profiler and Cloud Trace installed and configured by default. You want to determine which applications need performance tuning. What should you do?
Choose 2 answers
- A. 17 Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the local disk storage allocation.
- B. Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the memory resource allocation.
- C. Examine the wall-clock time and the CPU time Of the application. If the difference is substantial, increase the CPU resource allocation.
- D. Examine the heap usage Of the application. If the usage is low, mark the application for optimization.
- E. O Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization.
Answer: C,E
Explanation:
The correct answers are A and D.
Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the CPU resource allocation. This is a good way to determine if the application is CPU-bound, meaning that it spends more time waiting for the CPU than performing actual computation. Increasing the CPU resource allocation can improve the performance of CPU-bound applications1.
Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization. This is a good way to determine if the application is I/O-bound, meaning that it spends more time waiting for input/output operations than performing actual computation.
Increasing the CPU resource allocation will not help I/O-bound applications, and they may need optimization to reduce the number or duration of I/O operations2.
Answer B is incorrect because increasing the memory resource allocation will not help if the application is CPU-bound or I/O-bound. Memory allocation affects how much data the application can store and access in memory, but it does not affect how fast the application can process that data.
Answer C is incorrect because increasing the local disk storage allocation will not help if the application is CPU-bound or I/O-bound. Disk storage affects how much data the application can store and access on disk, but it does not affect how fast the application can process that data.
Answer E is incorrect because examining the heap usage of the application will not help to determine if the application needs performance tuning. Heap usage affects how much memory the application allocates for dynamic objects, but it does not affect how fast the application can process those objects. Moreover, low heap usage does not necessarily mean that the application is inefficient or unoptimized.
NEW QUESTION # 96
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?
- A. * 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.
- B. * 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.
- C. * 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.
- D. * 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.
Answer: B
NEW QUESTION # 97
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_dataset You 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?
- A. Create two aggregated log sinks at the organization level, and filter by project ID
- B. Create an aggregated Iog sink in the Dev and Prod folders
- C. Create a log sink in each project
- D. Create a single aggregated log sink at the organization level.
Answer: B
Explanation:
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.
NEW QUESTION # 98
You have a CI/CD pipeline that uses Cloud Build to build new Docker images and push them to Docker Hub. You use Git for code versioning. After making a change in the Cloud Build YAML configuration, you notice that no new artifacts are being built by the pipeline. You need to resolve the issue following Site Reliability Engineering practices. What should you do?
- A. Upload the configuration YAML file to Cloud Storage and use Error Reporting to identify and fix the issue.
- B. Disable the CI pipeline and revert to manually building and pushing the artifacts.
- C. Run a Git compare between the previous and current Cloud Build Configuration files to find and fix the bug.
- D. Change the CI pipeline to push the artifacts to Container Registry instead of Docker Hub.
Answer: C
NEW QUESTION # 99
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?
- A. Monitor results of Stackdriver Trace to determine the required amount of resources.
- B. Validate that the resource requirements are within the available quota limits of each region.
- C. Deploy the service in one region and use a global load balancer to route traffic to this region.
- D. Use the n1-highcpu-96 machine type in the configuration of the MIG.
Answer: B
Explanation:
https://cloud.google.com/compute/quotas#understanding_quotas
https://cloud.google.com/compute/quotas
NEW QUESTION # 100
You support a service with a well-defined Service Level Objective (SLO). Over the previous 6 months, your service has consistently met its SLO and customer satisfaction has been consistently high. Most of your service's operations tasks are automated and few repetitive tasks occur frequently. You want to optimize the balance between reliability and deployment velocity while following site reliability engineering best practices. What should you do? (Choose two.)
- A. Make the service's SLO more strict.
- B. Increase the service's deployment velocity and/or risk.
- C. Get the product team to prioritize reliability work over new features.
- D. Shift engineering time to other services that need more reliability.
- E. Change the implementation of your Service Level Indicators (SLIs) to increase coverage.
Answer: B,D
NEW QUESTION # 101
You manage a retail website for your company. The website consists of several microservices running in a GKE Standard node pool with node autoscaling enabled. Each microservice has resource limits and a Horizontal Pod Autoscaler configured. During a busy period, you receive alerts for one of the microservices.
When you check the Pods, half of them have the status OOMKilled, and the number of Pods is at the minimum autoscaling limit. You need to resolve the issue. What should you do?
- A. Update the node pool to use a machine type with more memory.
- B. Increase the maximum replica limit of the Horizontal Pod Autoscaler.
- C. Increase the maximum number of nodes in the node pool.
- D. Increase the memory resource limit of the microservice.
Answer: D
Explanation:
OOMKilled means the containers exceeded their allocated memory. Since the HPA isn't scaling up (stuck at min), this indicates the issue is not scale-related but container memory limits are too low.
"Pods terminated with OOMKilled have exceeded their memory limits. You should adjust the container's resource requests and limits."
- Kubernetes OOMKill Debug Guide
Scaling the number of replicas won't help if each replica crashes. You need to increase memory limits so pods can run reliably before autoscaling can even work.
NEW QUESTION # 102
Your organization is using Helm to package containerized applications Your applications reference both public and private charts Your security team flagged that using a public Helm repository as a dependency is a risk You want to manage all charts uniformly, with native access control and VPC Service Controls What should you do?
- A. Store public and private charts by using Git repository Configure Cloud Build to synchronize contents of the repository into a Cloud Storage bucket Connect Helm to the bucket by using https: // [bucket]
.srorage.googleapis.com/ [holnchart] as the Helm repository - B. Store public and private charts in OCI format by using Artifact Registry
- C. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend
- D. Store public and private charts by using GitHub Enterprise with Google Workspace as the identity provider
Answer: B
Explanation:
Explanation
The best option for managing all charts uniformly, with native access control and VPC Service Controls is to store public and private charts in OCI format by using Artifact Registry. Artifact Registry is a service that allows you to store and manage container images and other artifacts in Google Cloud. Artifact Registry supports OCI format, which is an open standard for storing container images and other artifacts such as Helm charts. You can use Artifact Registry to store public and private charts in OCI format and manage them uniformly. You can also use Artifact Registry's native access control features, such as IAM policies and VPC Service Controls, to secure your charts and control who can access them.
NEW QUESTION # 103
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