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Professional Cloud DevOps Engineer Practice Questions

Professional Cloud DevOps Engineer

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Question 1

You manage a critical API running on Cloud Run that serves an average of 10,000 requests per minute. You need to define service level objectives (SLOs) for availability and latency to ensure that the API meets user expectations. These include 99.9% availability and a maximum latency of 200 milliseconds for 95% of requests. You also need to ensure these SLOs are actively monitored and measured. What should you do?

Prioritize latency as the only SLO, targeting 100 ms for 99% of requests.
Configure Cloud Monitoring to send alerts when average API latency exceeds 150 ms or the error rate surpasses 0.1%.
Set SLOs for the API by using availability and latency service level indicators. Use Cloud Monitoring to track SLOs and alert on violations.
Set SLOs for availability at 99% and 500 ms latency for 90% of requests. Use Cloud Monitoring to track SLOs and alert on violations.

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Question 2

You are designing the hosting architecture in Google Kubernetes Engine (GKE) for business-critical applications. These applications provide custom metrics for monitoring with Prometheus. You need to collect the application metrics for alerting and troubleshooting purposes. You want to minimize the amount of manual effort and maintenance required while following Google-recommended practices. What should you do?

Configure Google Cloud Managed Service for Prometheus collection in the GKE clusters to collect the custom metrics.
Install Prometheus servers in the GKE clusters. Configure Prometheus to scrape and forward the custom metrics to the Cloud Monitoring API.
Modify each application to use the Cloud Monitoring API to push the custom metrics.
Install the OpenTelemetry collector in the GKE clusters. Configure it to collect and export the custom metrics to Cloud Monitoring.

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Question 3

You are developing a new containerized application that will be deployed to separate Google Kubernetes Engine (GKE) clusters for staging and production environments. You need to design a CI/CD pipeline that automatically builds and deploys the application to the staging environment whenever a developer commits code to the main branch. The pipeline must also include a manual approval step before any deployment to the production environment. You want to minimize operational overhead while following Google-recommended practices. What should you do?

Create two Cloud Build triggers. Configure the first trigger to build the container image, push it to Artifact Registry, and deploy it to the staging GKE cluster. Configure the second trigger to be invoked manually, which then deploys the same image to the production GKE cluster after a team member provides verbal approval.
Configure a Cloud Build trigger to build the container, and deploy it to the staging environment. For production deployments, create a separate Cloud Build trigger that checks out the same source code, rebuilds the container image with a _prod tag, and deploys it to the production GKE cluster. Require specific IAM permissions to run the production trigger as the approval mechanism.
Configure a Cloud Build trigger that builds a container image and pushes it to Artifact Registry on a commit to the main branch. Add a step to the build to create a Cloud Deploy release. Configure a Cloud Deploy delivery pipeline with a staging and production target, and set the requireApproval parameter to true for the production target.
Install Jenkins on a Compute Engine instance, and configure a webhook to trigger a job on a commit. In the job, build the container, push it to Artifact Registry, and use the kubectl apply command to deploy to staging. Use the built-in Jenkins input step to pause for manual approval before running kubectl apply for the production cluster.

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Question 4

You are deploying a new version of a critical, high-traffic web application running on a single Google Kubernetes Engine (GKE) cluster. The new version contains a significant feature update, but there is concern about potential bugs impacting user experience and revenue. You need to gradually roll this update out to users and roll back if error rates increase. What should you do?

Create a canary deployment in Cloud Deploy with one deployment target.
Create a standard deployment in Cloud Deploy with two deployment targets.
Create a standard deployment in Cloud Deploy with one deployment target.
Create a canary deployment in Cloud Deploy with two deployment targets.

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Question 5

Your company allows teams to self-manage Google Cloud projects, including project-level Identity and Access Management (IAM). You are concerned that the team responsible for the Shared VPC project might accidentally delete the project, so a lien has been placed on the project. You need to design a solution to restrict Shared VPC project deletion to those with the resourcemanager.projects.updateLiens permission at the organization level. What should you do?

Enable VPC Service Controls for the container.googleapis.com API service.
Instruct teams to only perform IAM permission management as code with Terraform.
Revoke the resourcemanager.projects.updateLiens permission from all users associated with the project.
Enable the compute.restrictXpnProjectLienRemoval organization policy constraint.

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Question 6

You are managing a complex microservices application running on Google Kubernetes Engine (GKE). You recently instrumented your services with OpenTelemetry to generate traces. During a performance investigation in Cloud Trace, you notice a transaction with high latency. The trace waterfall lacks the detail needed to identify the root cause, but you suspect the issue is within the business logic of the service. This service sends structured logs to Cloud Logging. You need to quickly analyze the logs that correspond to the high-latency request. What should you do?

Export all logs from Cloud Logging to BigQuery. Write a SQL query to join the trace data with the log data on the trace ID field, and then filter for the specific transaction to find the related log entries.
In the Cloud Trace waterfall view, identify the trace ID of a slow request. Then, in the Logs Explorer, write a query to filter for log entries that contain that specific trace ID.
Ensure your application's logging configuration includes the trace ID from the OpenTelemetry context in each structured log entry. Then, in the Cloud Trace details view for a slow request, click the "Show logs" option to view correlated logs directly alongside the trace's span waterfall.
Configure a log-based metric in Cloud Logging to count the occurrences of error messages. Then, create an alerting policy on this metric to be notified of future issues, and analyze the logs associated with those alerts.

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