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To prepare for the exam, candidates should have a solid understanding of cloud computing concepts and principles, as well as experience working with Google Cloud Platform services such as Compute Engine, Kubernetes Engine, Cloud Storage, and Cloud Pub/Sub. Additionally, candidates should have experience with DevOps methodologies and tools such as Git, Jenkins, and Ansible.

Google Professional-Cloud-DevOps-Engineer Certification Exam is a highly respected certification in the field of cloud computing. It is designed to validate the skills and knowledge of professionals who are responsible for building and managing cloud-based solutions using Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam requires a deep understanding of DevOps practices, as well as a comprehensive knowledge of Google Cloud Platform tools and services.

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Google Professional-Cloud-DevOps-Engineer Certification is a valuable credential for professionals seeking to advance their careers in DevOps and cloud computing. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification demonstrates the candidate's ability to deploy and manage applications using best practices and tools on the Google Cloud Platform, which is becoming increasingly popular among enterprises. Moreover, the certification is recognized globally, making it an excellent opportunity for professionals who want to work with global clients or organizations.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
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?

A. Enable high availability on the CloudSQL instances.
B. Modify the App Engine configuration to have additional idle instances.
C. Move the application from App Engine to Compute Engine.
D. Upgrade the GCS buckets to Multi-Regional.

Answer: B

Explanation:
Scaling App Engine scales the number of instances automatically in response to processing volume. This scaling factors in the automatic_scaling settings that are provided on a per-version basis in the configuration file. A service with basic scaling is configured by setting the maximum number of instances in the max_instances parameter of the basic_scaling setting. The number of live instances scales with the processing volume. You configure the number of instances of each version in that service's configuration file. The number of instances usually corresponds to the size of a dataset being held in memory or the desired throughput for offline work. You can adjust the number of instances of a manually-scaled version very quickly, without stopping instances that are currently running, using the Modules API set_num_instances function. https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed
https://cloud.google.com/appengine/docs/standard/python/config/appref
max_idle_instances Optional. The maximum number of idle instances that App Engine should maintain for this version. Specify a value from 1 to 1000. If not specified, the default value is automatic, which means App Engine will manage the number of idle instances. Keep the following in mind: A high maximum reduces the number of idle instances more gradually when load levels return to normal after a spike. This helps your application maintain steady performance through fluctuations in request load, but also raises the number of idle instances (and consequent running costs) during such periods of heavy load.

NEW QUESTION # 84
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?

A. 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
B. Runthegcioud run services update-traffic booking-engine -to-revisions LATEST*! command Use the ht tps: //booking-engine-abcdef. a. run. ape URL for testing
C. 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
D. Runthegcioud run deploy booking-engine -no-traffic --ag dev command Use the https://dev----booking-engine-abcdef. a. run. app URL for testing

Answer: B

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.

NEW QUESTION # 85
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. Add the Logs Writer role to the service account.
C. Enable Private Google Access on the subnet that the instance is in.
D. Export the service account key and configure the agents to use the key.

Answer: B

Explanation:
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 # 86
You are managing an application that runs in Compute Engine The application uses a custom HTTP server to expose an API that is accessed by other applications through an internal TCP/UDP load balancer A firewall rule allows access to the API port from 0.0.0-0/0. You need to configure Cloud Logging to log each IP address that accesses the API by using the fewest number of steps What should you do Bret?

A. Enable logging on the firewall rule
B. Install the Ops Agent on the Compute Engine instances.
C. Enable Packet Mirroring on the VPC
D. Enable VPC Flow Logs on the subnet

Answer: A

Explanation:
The best option for configuring Cloud Logging to log each IP address that accesses the API by using the fewest number of steps is to enable logging on the firewall rule. A firewall rule is a rule that controls the traffic to and from your Compute Engine instances. You can enable logging on a firewall rule to capture information about the traffic that matches the rule, such as source and destination IP addresses, protocols, ports, and actions. You can use Cloud Logging to view and export the firewall logs to other destinations, such as BigQuery, for further analysis.

NEW QUESTION # 87
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?

A. Add a new instance template update the managed instance group to use the new instance template and delete the old instance template
B. Set the create_bef ore_destroy meta-argument to true in the lifecycle block on the instance template
C. Delete the managed instance group and recreate it after updating the instance template
D. Remove the managed instance group from the Terraform state file update the instance template and reimport the managed instance group.

Answer: B

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.

NEW QUESTION # 88
......

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Simplest Format of Google Professional-Cloud-DevOps-Engineer Exam PDF Practice Materials

Google Professional-Cloud-DevOps-Engineer Practice Test Learning Material in Three Different Formats

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q83-Q88):

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