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[Dec 14, 2025] Professional-Cloud-DevOps-Engineer Exam Dumps PDF Updated Dump from PassTestking Guaranteed Success

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The Google Professional Cloud DevOps Engineer certification demonstrates your ability to efficiently perform the development operations that can balance service reliability and delivery speed. After completing the qualifying exam, you will be proficient in using Google Cloud Platform to build software delivery pipelines, deploy & monitor services, and manage incidents. This expertise will give you access to endless opportunities for career development. Immediately after getting certified, you can start a career as a Cloud DevOps Engineer. Additionally, you can apply for the related job roles, such as a DevOps Infrastructure Engineer, a Cloud Solutions Architect, a GCP Cloud Native Architect, a Google Cloud Platform Data Architect, a GCP Cloud Data Engineer, and more. The average salary that the certificate holders can earn ranges between $137,000 and $180,000 per year.

NO.94 You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?

Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.

Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application’s pods and write to Slackdriver Logging.

Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application’s pods and write to Stackdriver Logging.

Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application’s pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.

NO.95 Your company is using HTTPS requests to trigger a public Cloud Run-hosted service accessible at the
https://booking-engine-abcdef .a.run.app URL You need to give developers the ability to test the latest revisions of the service before the service is exposed to customers What should you do?

Runthegcioud run deploy booking-engine -no-traffic –ag dev command Use the
https://dev—-booking-engine-abcdef. a. run. app URL for testing

Runthegcioud run services update-traffic booking-engine -to-revisions LATEST*! command Use the ht tps: //booking-engine-abcdef. a. run. ape URL for testing

Pass the curl -K “Authorization: Hearer S(gclcud auth print-identity-token)” auth token Use the https: /
/booking-engine-abcdef. a. run. app URL to test privately

Grant the roles/run. invoker role to the developers testing the booking-engine service Use the https:
//booking-engine-abcdef. private. run. app URL for testing

Explanation
The best option for securing the CI/CD deployment pipeline is to configure vulnerability analysis with Artifact Registry and Binary Authorization. Vulnerability analysis is a feature that allows you to scan container images for known vulnerabilities and security issues. You can use vulnerability analysis with Artifact Registry, which is a service that allows you to store and manage container images and other artifacts. By using vulnerability analysis with Artifact Registry, you can ensure that your container images are scanned for vulnerabilities before they are deployed. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization with Cloud Build, which is a service that allows you to build and deploy container images. By using Binary Authorization with Cloud Build, you can ensure that only authorized and verified container images are deployed to your environment.

NO.96 You are building an application that runs on Cloud Run The application needs to access a third-party API by using an API key You need to determine a secure way to store and use the API key in your application by following Google-recommended practices What should you do?

Save the API key in Secret Manager as a secret Reference the secret as an environment variable in the Cloud Run application

Save the API key in Secret Manager as a secret key Mount the secret key under the /sys/api_key directory and decrypt the key in the Cloud Run application

Save the API key in Cloud Key Management Service (Cloud KMS) as a key Reference the key as an environment variable in the Cloud Run application

Encrypt the API key by using Cloud Key Management Service (Cloud KMS) and pass the key to Cloud Run as an environment variable Decrypt and use the key in Cloud Run

The best option for storing and using the API key in your application by following Google-recommended practices is to save the API key in Secret Manager as a secret and reference the secret as an environment variable in the Cloud Run application. Secret Manager is a service that allows you to store and manage sensitive data, such as API keys, passwords, and certificates, in Google Cloud. A secret is a resource that represents a logical secret, such as an API key. You can save the API key in Secret Manager as a secret and use IAM policies to control who can access it. You can also reference the secret as an environment variable in the Cloud Run application by using the ${SECRET_NAME} syntax. This way, you can securely store and use the API key in your application without exposing it in your code or configuration files.

NO.97 Your application images are built and pushed to Google Container Registry (GCR). You want to build an automated pipeline that deploys the application when the image is updated while minimizing the development effort. What should you do?

Use Cloud Build to trigger a Spinnaker pipeline.

Use Cloud Pub/Sub to trigger a Spinnaker pipeline.

Use a custom builder in Cloud Build to trigger a Jenkins pipeline.

Use Cloud Pub/Sub to trigger a custom deployment service running in Google Kubernetes Engine (GKE).

Explanation
https://cloud.google.com/architecture/continuous-delivery-toolchain-spinnaker-cloud
https://spinnaker.io/guides/user/pipeline/triggers/pubsub/
The most efficient way to build an automated pipeline that deploys the application when the image is updated is to use Cloud Pub/Sub to trigger a Spinnaker pipeline. This way, you can leverage the built-in integration between GCR and Cloud Pub/Sub, and use Spinnaker as a continuous delivery platform for deploying your application .

NO.98 Your company is using HTTPS requests to trigger a public Cloud Run-hosted service accessible at the https://booking-engine-abcdef .a.run.app URL You need to give developers the ability to test the latest revisions of the service before the service is exposed to customers What should you do?

Runthegcioud run deploy booking-engine -no-traffic –ag dev command Use the https://dev—-booking-engine-abcdef. a. run. app URL for testing

Runthegcioud run services update-traffic booking-engine -to-revisions LATEST*! command Use the ht tps: //booking-engine-abcdef. a. run. ape URL for testing

Pass the curl -K “Authorization: Hearer S(gclcud auth print-identity-token)” auth token Use the https: / /booking-engine-abcdef. a. run. app URL to test privately

Grant the roles/run. invoker role to the developers testing the booking-engine service Use the https: //booking-engine-abcdef. private. run. app URL for testing

NO.99 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?

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.

Look through other metrics related to the product and find SLOs with remaining error budget. Reallocate the error budgets and allow the feature launch.

Escalate the situation and request additional error budget.

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.

The correct answer is
A, 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.
According to the Site Reliability Engineering (SRE) practices, an error budget is the amount of unreliability that a service can tolerate without harming user satisfaction1. An error budget is derived from the service-level objectives (SLOs), which are the measurable goals for the service quality2. When a service exceeds its error budget, it means that it has violated its SLOs and may have negatively impacted the users. In this case, the SRE team should notify the product team that their error budget is used up and negotiate with them for a launch freeze or a lower SLO3. A launch freeze means that no new features are deployed until the service reliability is restored. A lower SLO means that the product team accepts a slightly worse user experience in exchange for launching new features. Both options require a trade-off between reliability and innovation, and should be agreed upon by both teams.
The other options are incorrect because they do not follow the SRE practices. Option B is incorrect because it violates the principle of error budget autonomy, which means that each service should have its own error budget and SLOs, and should not borrow or reallocate them from other services4. Option C is incorrect because it does not address the root cause of the error budget overspend, and may create unrealistic expectations for the service reliability. Option D is incorrect because it does not prevent the possibility of introducing new errors or bugs with the feature launch, which may further degrade the service quality and user satisfaction.
Reference:
Error Budgets, Error Budgets. Service Level Objectives, Service Level Objectives. Error Budget Policies, Error Budget Policies. Error Budget Autonomy, Error Budget Autonomy.

NO.100 You use Terraform to manage an application deployed to a Google Cloud environment The application runs on instances deployed by a managed instance group The Terraform code is deployed by using a CI/CD pipeline When you change the machine type on the instance template used by the managed instance group, the pipeline fails at the terraform apply stage with the following error message

You need to update the instance template and minimize disruption to the application and the number of pipeline runs What should you do?

Delete the managed instance group and recreate it after updating the instance template

Add a new instance template update the managed instance group to use the new instance template and delete the old instance template

Remove the managed instance group from the Terraform state file update the instance template and reimport the managed instance group.

Set the create_bef ore_destroy meta-argument to true in the lifecycle block on the instance template

Explanation
The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.

NO.101 You are running an application on Compute Engine and collecting logs through Stackdriver. You discover that some personally identifiable information (Pll) is leaking into certain log entry fields. All Pll entries begin with the text userinfo. You want to capture these log entries in a secure location for later review and prevent them from leaking to Stackdriver Logging. What should you do?

Create a basic log filter matching userinfo, and then configure a log export in the Stackdriver console with Cloud Storage as a sink.

Use a Fluentd filter plugin with the Stackdriver Agent to remove log entries containing userinfo, and then copy the entries to a Cloud Storage bucket.

Create an advanced log filter matching userinfo, configure a log export in the Stackdriver console with Cloud Storage as a sink, and then configure a tog exclusion with userinfo as a filter.

Use a Fluentd filter plugin with the Stackdriver Agent to remove log entries containing userinfo, create an advanced log filter matching userinfo, and then configure a log export in the Stackdriver console with Cloud Storage as a sink.

https://medium.com/google-cloud/fluentd-filter-plugin-for-google-cloud-data-loss-prevention-api-42bbb1308e76

NO.102 Your application images are built using Cloud Build and pushed to Google Container Registry (GCR). You want to be able to specify a particular version of your application for deployment based on the release version tagged in source control. What should you do when you push the image?

Reference the image digest in the source control tag.

Supply the source control tag as a parameter within the image name.

Use Cloud Build to include the release version tag in the application image.

Use GCR digest versioning to match the image to the tag in source control.

NO.103 Your team is building a service that performs compute-heavy processing on batches of data The data is processed faster based on the speed and number of CPUs on the machine These batches of data vary in size and may arrive at any time from multiple third-party sources You need to ensure that third partiesare able to upload their data securely. You want to minimize costs while ensuring that the data is processed as quickly as possible What should you do?

* Provide a secure file transfer protocol (SFTP) server on a Compute Engine instance so that thirdparties can upload batches of data and provide appropriate credentials to the server* Create a Cloud Function with a google.storage, object, finalize Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group* Use an image pre-loaded with the data processing software that terminates the instances when processing completes

* Provide a Cloud Storage bucket so that third parties can upload batches of data, and provideappropriate Identity and Access Management (1AM) access to the bucket* Use a standard Google Kubernetes Engine (GKE) cluster and maintain two services one that processes the batches of data and one that monitors Cloud Storage for new batches of data* Stop the processing service when there are no batches of data to process

* Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate identity and Access Management (1AM) access to the bucket* Create a Cloud Function with a google, storage, object .finalise Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group* Use an image pre-loaded with the data processing software that terminates the instances when processing completes

* Provide a Cloud Storage bucket so that third parties can upload batches of data, and provideappropriate Identity and Access Management (1AM) access to the bucket* Use Cloud Monitoring to detect new batches of data in the bucket and trigger a Cloud Function that processes the data* Set a Cloud Function to use the largest CPU possible to minimize the runtime of the processing

NO.104 Your application services run in Google Kubernetes Engine (GKE). You want to make sure that only images from your centrally-managed Google Container Registry (GCR) image registry in the altostrat-images project can be deployed to the cluster while minimizing development time. What should you do?

Create a custom builder for Cloud Build that will only push images to gcr.io/altostrat-images.

Use a Binary Authorization policy that includes the whitelist name pattern gcr.io/attostrat-images/.

Add logic to the deployment pipeline to check that all manifests contain only images from gcr.io/altostrat-images.

Add a tag to each image in gcr.io/altostrat-images and check that this tag is present when the image is deployed.

NO.105 Your team is designing a new application for deployment both inside and outside Google Cloud Platform (GCP). You need to collect detailed metrics such as system resource utilization. You want to use centralized GCP services while minimizing the amount of work required to set up this collection system. What should you do?

Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.

Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.

Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.

Install an Application Performance Monitoring (APM) tool in both locations, and configure an export to a central data storage location for analysis.

Explanation/Reference:

NO.106 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?

Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.

Check the metric by using Metrics Explorer.

Locate all the Pods with emptyDir volumes. use the df-h command to measure volume disk usage.

Locate all the Pods with emptyDir volumes. Use the du -sh * command to measure volume disk usage.

The correct answer is A, Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.
The node/ephemeral_storage/used_bytes metric reports the total amount of ephemeral storage used by Pods on each node1. You can use Metrics Explorer to query and visualize this metric and filter it by node name, namespace, or Pod name2. This way, you can identify which Pods are consuming the most ephemeral storage and causing disk pressure on the nodes. You do not need to have execute access to the workloads or nodes to use Metrics Explorer.
The other options are incorrect because they require execute access to the workloads or nodes, which you do not have. The df -h and du -sh * commands are Linux commands that can measure disk usage, but you need to run them inside the Pods or on the nodes, which is not possible in your scenario34.
Reference:
Monitoring metrics for Kubernetes system components, Node metrics, node/ephemeral_storage/used_bytes. Using Metrics Explorer, Querying metrics. How do I find out disk space utilization information using Linux command line?, df command. How to check disk space in Linux from the command line, du command.

NO.107 Your applications performance in Google Cloud has degraded since the last release You suspect that downstream dependencies might be causing some requests to take longer to complete You need to investigate the issue with your application to determine the cause What should you do?

Configure Error Reporting in your application

Configure Google Cloud Managed Service for Prometheus in your application

Configure Cloud Profiler in your application

Configure Cloud Trace in your application

The best option for investigating the issue with your application’s performance in Google Cloud is to configure Cloud Trace in your application. Cloud Trace is a service that allows you to collect and analyze latency data from your application. You can use Cloud Trace to trace requests across different components of your application, such as downstream dependencies, and identify where they take longer to complete. You can also use Cloud Trace to compare latency data across different versions of your application, and detect any performance degradation or improvement. By using Cloud Trace, you can diagnose and troubleshoot performance issues with your application in Google Cloud.

NO.108 Your application artifacts are being built and deployed via a CI/CD pipeline. You want the CI/CD pipeline to securely access application secrets. You also want to more easily rotate secrets in case of a security breach. What should you do?

Prompt developers for secrets at build time. Instruct developers to not store secrets at rest.

Store secrets in a separate configuration file on Git. Provide select developers with access to the configuration file.

Store secrets in Cloud Storage encrypted with a key from Cloud KMS. Provide the CI/CD pipeline with access to Cloud KMS via IAM.

Encrypt the secrets and store them in the source code repository. Store a decryption key in a separate repository and grant your pipeline access to it

NO.109 Your company has a Google Cloud resource hierarchy with folders for production test and development Your cyber security team needs to review your company’s Google Cloud security posture to accelerate security issue identification and resolution You need to centralize the logs generated by Google Cloud services from all projects only inside your production folder to allow for alerting and near-real time analysis. What should you do?

Enable the Workflows API and route all the logs to Cloud Logging

Create a central Cloud Monitoring workspace and attach all related projects

Create an aggregated log sink associated with the production folder that uses a Pub Sub topic as the destination

Create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination

The best option for centralizing the logs generated by Google Cloud services from all projects only inside your production folder is to create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. A Cloud Logging bucket is a storage location for logs that can be used as a destination for log sinks. By creating an aggregated log sink with a Cloud Logging bucket, you can collect and store all the logs from the production folder in one place and allow for alerting and near-real time analysis using Cloud Monitoring and Cloud Operations.

NO.110 You are designing a system with three different environments: development, quality assurance (QA), and production.
Each environment will be deployed with Terraform and has a Google Kubemetes Engine (GKE) cluster created so that application teams can deploy their applications. Anthos Config Management will be used and templated to deploy infrastructure level resources in each GKE cluster. All users (for example, infrastructure operators and application owners) will use GitOps. How should you structure your source control repositories for both Infrastructure as Code (laC) and application code?

Cloud Infrastructure (Terraform) repository is shared: different directories are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: differentoverlay directories are different environmentsApplication (app source code) repositories are separated: different branches are different features

Cloud Infrastructure (Terraform) repository is shared: different directories are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:different branches are different environmentsApplication (app source code) repositories are separated: different branches are different features

Cloud Infrastructure (Terraform) repository is shared: different branches are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: differentoverlay directories are different environmentsApplication (app source code) repository is shared: different directories are different features

Cloud Infrastructure (Terraform) repositories are separated: different branches are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:different overlay directories are different environmentsApplication (app source code) repositories are separated: different branches are different features

The correct answer is B. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments. GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different branches are different environments. Application (app source code) repositories are separated: different branches are different features.
This answer follows the best practices for using Terraform and Anthos Config Management with GitOps, as described in the following sources:
For Terraform, it is recommended to use a single repository for all environments, and use directories to separate them. This way, you can reuse the same Terraform modules and configurations across environments, and avoid code duplication and drift.You can also use Terraform workspaces to isolate the state files for each environment12.
For Anthos Config Management, it is recommended to use separate repositories for each environment, and use branches to separate the clusters within each environment. This way, you can enforce different policies and configurations for each environment, and use pull requests to promote changes across environments.You can also use Kustomize to create overlays for each cluster that apply specific patches or customizations34.
For application code, it is recommended to use separate repositories for each application, and use branches to separate the features or bug fixes for each application. This way, you can isolate the development and testing of each application, and use pull requests to merge changes into the main branch.You can also use tags or labels to trigger deployments to different environments5.
References:
1:Best practices for using Terraform | Google Cloud
2: Terraform Recommended Practices – Part 1 | Terraform – HashiCorp Learn
3:Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
4: Using Kustomize with Anthos Config Management | Anthos Config Management Documentation | Google Cloud
5: Deploy Anthos on GKE with Terraform part 3: Continuous Delivery with Cloud Build | Google Cloud Blog GitOps-style continuous delivery with Cloud Build | Cloud Build Documentation | Google Cloud

NO.111 Your company follows Site Reliability Engineering practices. You are the person in charge of Communications for a large, ongoing incident affecting your customer-facing applications. There is still no estimated time for a resolution of the outage. You are receiving emails from internal stakeholders who want updates on the outage, as well as emails from customers who want to know what is happening. You want to efficiently provide updates to everyone affected by the outage. What should you do?

Focus on responding to internal stakeholders at least every 30 minutes. Commit to “next update” times.

Provide periodic updates to all stakeholders in a timely manner. Commit to a “next update” time in all communications.

Delegate the responding to internal stakeholder emails to another member of the Incident Response Team. Focus on providing responses directly to customers.

Provide all internal stakeholder emails to the Incident Commander, and allow them to manage internal communications. Focus on providing responses directly to customers.

When disaster strikes, the person who declares the incident typically steps into the IC role and directs the high-level state of the incident. The IC concentrates on the 3Cs and does the following: Commands and coordinates the incident response, delegating roles as needed. By default, the IC assumes all roles that have not been delegated yet. Communicates effectively. Stays in control of the incident response. Works with other responders to resolve the incident. https://sre.google/workbook/incident-response/

NO.112 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?

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.

When there is a change in GitHub, use a webhook to send a request to Cloud Service Mesh, and apply the change.

Configure Config Sync with the GitHub repository. When there is a change in the repository, use Config Sync to apply the change.

Configure Config Connector with the GitHub repository. When there is a change in the repository, use Config Connector to apply the change.

NO.113 You support a web application that runs on App Engine and uses CloudSQL and Cloud Storage for data storage. After a short spike in website traffic, you notice a big increase in latency for all user requests, increase in CPU use, and the number of processes running the application. Initial troubleshooting reveals:
After the initial spike in traffic, load levels returned to normal but users still experience high latency.
Requests for content from the CloudSQL database and images from Cloud Storage show the same high latency.
No changes were made to the website around the time the latency increased.
There is no increase in the number of errors to the users.
You expect another spike in website traffic in the coming days and want to make sure users don’t experience latency. What should you do?

Upgrade the GCS buckets to Multi-Regional.

Enable high availability on the CloudSQL instances.

Move the application from App Engine to Compute Engine.

Modify the App Engine configuration to have additional idle instances.

NO.114 Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to a Kubernetes cluster in the production environment. The security auditor is concerned that developers or operators could circumvent automated testing and push code changes to production without approval. What should you do to enforce approvals?

Configure the build system with protected branches that require pull request approval.

Use an Admission Controller to verify that incoming requests originate from approved sources.

Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.

Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.

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