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Upon successful completion of the certification exam, candidates will receive a Google Cloud Certified - Professional Cloud DevOps Engineer certificate, which demonstrates their proficiency in cloud-based DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is recognized globally and is highly valued by employers, making it an excellent investment for professionals seeking to advance their career in this field.

The Google Professional Cloud DevOps Engineer exam is taken by the specialists who want to obtain international certification and get validated as professional engineers who can manage Cloud DevOps features.

Google Professional-Cloud-DevOps-Engineer Certification Exam is an industry-recognized credential that validates the skills and knowledge of professionals in the field of DevOps. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification demonstrates that candidates have the ability to design and implement robust and scalable DevOps solutions using Google Cloud technologies. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam covers a broad range of topics including continuous integration and delivery, infrastructure as code, monitoring and logging, and automation of various DevOps processes.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
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?

A. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
B. Use an Admission Controller to verify that incoming requests originate from approved sources.
C. Configure the build system with protected branches that require pull request approval.
D. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.

Answer: A

Explanation:
Explanation
The keywords here is "developers or operators". Option A the operators could push images to production without approval (operators could touch the cluster directly and the cluster cannot do any action against them).
Rest same as francisco_guerra.

NEW QUESTION # 102
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. Update the instance to use the default Compute Engine service account.
B. Enable Private Google Access on the subnet that the instance is in.
C. Export the service account key and configure the agents to use the key.
D. Add the Logs Writer role to the service account.

Answer: D

Explanation:
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.
Explanation:
The correct answer is
Reference:
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 # 103
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 the production environment. A recent security audit alerted your team that the code pushed to production could contain vulnerabilities and that the existing tooling around virtual machine (VM) vulnerabilities no longer applies to the containerized environment. You need to ensure the security and patch level of all code running through the pipeline. What should you do?

A. Reconfigure the existing operating system vulnerability software to exist inside the container.
B. Set up Container Analysis to scan and report Common Vulnerabilities and Exposures.
C. Implement static code analysis tooling against the Docker files used to create the containers.
D. Configure the containers in the build pipeline to always update themselves before release.

Answer: C

Explanation:
Explanation
https://cloud.google.com/binary-authorization
Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run. With Binary Authorization, you can require images to be signed by trusted authorities during the development process and then enforce signature validation when deploying. By enforcing validation, you can gain tighter control over your container environment by ensuring only verified images are integrated into the build-and-release process.

NEW QUESTION # 104
Your application images are built wing 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 would you do when you push the image?

A. Reference the image digest in the source control tag.
B. Supply the source control tag as a parameter within the image name.
C. Use GCR digest versioning to match the image to the tag in source control.
D. Use Cloud Build to include the release version tag in the application image.

Answer: D

NEW QUESTION # 105
You are performing a semi-annual capacity planning exercise for your flagship service You expect a service user growth rate of 10% month-over-month for the next six months Your service is fully containerized and runs on a Google Kubemetes Engine (GKE) standard cluster across three zones with cluster autoscaling enabled You currently consume about 30% of your total deployed CPU capacity and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth o' as a result of zone failure while you avoid unnecessary costs How should you prepare to handle the predicted growth?

A. Because you deployed the service on GKE and are using a cluster autoscaler your GKE cluster will scale automatically regardless of growth rate
B. Proactively add 80% more node capacity to account for six months of 10% growth rate and then perform a load test to ensure that you have enough capacity
C. Because you are only using 30% of deployed CPU capacity there is significant headroom and you do not need to add any additional capacity for this rate of growth
D. Verify the maximum node pool size enable a Horizontal Pod Autoscaler and then perform a load lest to verify your expected resource needs

Answer: D

Explanation:
The best option for preparing to handle the predicted growth is to verify the maximum node pool size, enable a Horizontal Pod Autoscaler, and then perform a load test to verify your expected resource needs. The maximum node pool size is a parameter that specifies the maximum number of nodes that can be added to a node pool by the cluster autoscaler. You should verify that the maximum node pool size is sufficient to accommodate your expected growth rate and avoid hitting any quota limits. 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. You should enable a Horizontal Pod Autoscaler for your application to ensure that it runs enough Pods to handle the load. A load test is a test that simulates high user traffic and measures the performance and reliability of your application. You should perform a load test to verify your expected resource needs and identify any bottlenecks or issues.

NEW QUESTION # 106
......

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Free PDF Quiz Google - Professional-Cloud-DevOps-Engineer - Professional Test Google Cloud Certified - Professional Cloud DevOps Engineer Exam King

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