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To prepare for the exam, candidates will need to have a strong understanding of cloud computing, as well as experience with DevOps processes and tools. Google Cloud offers a range of training courses and resources to help candidates prepare for the exam, including online courses, hands-on labs, and practice exams. Candidates can also benefit from working with a study group or seeking the guidance of a certified professional.

To qualify for the Google Professional-Cloud-DevOps-Engineer Certification Exam, candidates must have at least three years of experience working in a DevOps engineering role and a minimum of one year of experience working with Google Cloud. Candidates are also expected to have a solid understanding of software development principles and practices, as well as experience with scripting languages such as Python and Bash.

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Google Professional-Cloud-DevOps-Engineer certification exam is designed for professionals who want to demonstrate their expertise in the Google Cloud Platform (GCP) and DevOps practices. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam focuses on testing the candidate's ability to design, develop, and manage GCP solutions using DevOps principles. Professional-Cloud-DevOps-Engineer Exam is intended for professionals who have experience in developing and managing applications on GCP and have a good understanding of DevOps practices and principles.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q165-Q170):

NEW QUESTION # 165
You manage several production systems that run on Compute Engine in the same Google Cloud Platform (GCP) project. Each system has its own set of dedicated Compute Engine instances. You want to know how must it costs to run each of the systems. What should you do?

A. Name each virtual machine (VM) after the system it runs. Set up a usage report export to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs based on VM name.
B. Assign all instances a label specific to the system they run. Configure BigQuery billing export and query costs per label.
C. Enrich all instances with metadata specific to the system they run. Configure Stackdriver Logging to export to BigQuery, and query costs based on the metadata.
D. In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.

Answer: B

NEW QUESTION # 166
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?

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

Answer: B

NEW QUESTION # 167
You created a Stackdriver chart for CPU utilization in a dashboard within your workspace project. You want to share the chart with your Site Reliability Engineering (SRE) team only. You want to ensure you follow the principle of least privilege. What should you do?

A. Click "Share chart by URL" and provide the URL to the SRE team. Assign the SRE team the Monitoring Viewer IAM role in the workspace project.
B. Share the workspace Project ID with the SRE team. Assign the SRE team the Dashboard Viewer IAM role in the workspace project.
C. Share the workspace Project ID with the SRE team. Assign the SRE team the Monitoring Viewer IAM role in the workspace project.
D. Click "Share chart by URL" and provide the URL to the SRE team. Assign the SRE team the Dashboard Viewer IAM role in the workspace project.

Answer: A

Explanation:
Explanation
https://cloud.google.com/monitoring/access-control

NEW QUESTION # 168
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?

A. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
B. 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.
C. 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.
D. 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.

Answer: D

Explanation:
Explanation
https://cloud.google.com/architecture/customizing-stackdriver-logs-fluentd Besides the list of default logs that the Logging agent streams by default, you can customize the Logging agent to send additional logs to Logging or to adjust agent settings by adding input configurations. The configuration definitions in these sections apply to the fluent-plugin-google-cloud output plugin only and specify how logs are transformed and ingested into Cloud Logging.
https://cloud.google.com/logging/docs/agent/logging/configuration#configure

NEW QUESTION # 169
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?

A. 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
B. 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
C. 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
D. 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

Answer: D

Explanation:
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

NEW QUESTION # 170
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