Professional-Cloud-DevOps-Engineer Exam Dumps - Google Cloud DevOps Engineer Questions and Answers

For a postmortem to be truly blameless, it must focus on identifying the contributing causes of the incident without indicting any individual or team for bad or inappropriate behavior.

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.

The correct answer is B. Apply the constraints/iam.disableServiceAccountKeyCreation constraint to the organization.

According to the Google Cloud documentation, the constraints/iam.disableServiceAccountKeyCreation constraint is an organization policy constraint that prevents the creation of user-managed service account keys1.User-managed service account keys are long-lived credentials that can be downloaded as JSON or P12 files and used to authenticate as a service account2.These keys pose severe security risks if they are leaked, stolen, or misused by unauthorized entities34.By applying this constraint to the organization, you can completely eliminate the risks associated with the use of JSON service account keys and enforce a more secure alternative for authentication, such as Workload Identity or short-lived access tokens12. This also minimizes operational overhead by avoiding the need to manage, rotate, or revoke user-managed service account keys.

The other options are incorrect because they do not completely eliminate the risks associated with the use of JSON service account keys. Option A is incorrect because it only restricts the IAM permissions to create, list, get, delete, or sign service account keys, but it does not prevent existing keys from being used or leaked. Option C is incorrect because it only disables the upload of user-managed service account keys, but it does not prevent the creation or download of such keys. Option D is incorrect because it only limits the IAM role that can create and manage service account keys, but it does not prevent the keys from being distributed or exposed to unauthorized entities.

Comprehensive and Detailed Explanation:

Cloud Run scales down to zero when idle. If CPU is allocated only during request processing, background telemetry tasks (like OpenTelemetry traces) may not complete before the container shuts down.

Solution: Configure CPU to be always-allocated → This ensures that background tasks (like trace exports) continue even when the service is idle.

????Why not other options?

A (Increasing CPU to 4 cores)❌→ More CPU won’t help if traces are being cut off due to shutdown.

B (Using an HTTP health check)❌→ Doesn’t affect OpenTelemetry traces.

C (CPU only during request processing)❌→ This causes the problem because traces need CPU after request completion to be exported.

????Official Reference:

Cloud Run Tracing with OpenTelemetry

Cloud Run Always-Allocated CPU

The most cost-effective network tier and load balancing configuration for your frontend tier is to use Premium Tier with a regional load balancer. Premium Tier is a network tier that provides high-performance and low-latency network connectivity across Google’s global network. A regional load balancer is a load balancer that distributes traffic within a single region. Since your application is deployed entirely within the europe-west2 region and only serves users based in the United Kingdom, you can use Premium Tier with a regional load balancer to optimize the network performance and cost.

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.

https://www.atlassian.com/incident-management/kpis/common-metrics

https://linkedin.github.io/school-of-sre/

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.

"A Project can host many Projects and appear in many Projects, but it can only be used as the scoping project once. We recommend that you create a new Project for the purpose of having multiple Projects in the same scope."

The best option for storing all logs for one year and minimizing required code changes is to create a logs bucket and logging sink, set the retention on the logs bucket to 365 days, configure the logging sink to send logs to the bucket, and give your client access to the bucket to retrieve the logs. A logs bucket is a Cloud Storage bucket that is used to store logs from Cloud Logging. A logging sink is a resource that defines where log entries are sent, such as a logs bucket, BigQuery dataset, or Pub/Sub topic. You can create a logs bucket and logging sink in Cloud Logging and set the retention on the logs bucket to 365 days. This way, you can ensure that all logs are stored for one year and protected from deletion. You can also configure the logging sink to send logs from Cloud Run and Cloud Functions to the logs bucket without any code changes. You can then give your client access to the logs bucket by using IAM policies or signed URLs.