PROFESSIONAL-CLOUD-DEVOPS-ENGINEER Online Practice Questions and Answers

You support a Node.js application running on Google Kubernetes Engine (GKE) in production. The application makes several HTTP requests to dependent applications. You want to anticipate which dependent applications might cause performance issues. What should you do?

A. Instrument all applications with Stackdriver Profiler.

B. Instrument all applications with Stackdriver Trace and review inter-service HTTP requests.

C. Use Stackdriver Debugger to review the execution of logic within each application to instrument all applications.

D. Modify the Node.js application to log HTTP request and response times to dependent applications. Use Stackdriver Logging to find dependent applications that are performing poorly.

You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline. Your application has an in-memory cache that loads objects at start time. You want to automate the comparison of the canary version against the production version. How should you configure the canary analysis?

A. Compare the canary with a new deployment of the current production version.

B. Compare the canary with a new deployment of the previous production version.

C. Compare the canary with the existing deployment of the current production version.

D. Compare the canary with the average performance of a sliding window of previous production versions.

You support an e-commerce application that runs on a large Google Kubernetes Engine (GKE) cluster deployed on-premises and on Google Cloud Platform. The application consists of microservices that run in containers. You want to identify containers that are using the most CPU and memory. What should you do?

A. Use Stackdriver Kubernetes Engine Monitoring.

B. Use Prometheus to collect and aggregate logs per container, and then analyze the results in Grafana.

C. Use the Stackdriver Monitoring API to create custom metrics, and then organize your containers using groups.

D. Use Stackdriver Logging to export application logs to BigQuery, aggregate logs per container, and then analyze CPU and memory consumption.

You need to deploy a new service to production. The service needs to automatically scale using a Managed Instance Group (MIG) and should be deployed over multiple regions. The service needs a large number of resources for each instance and you need to plan for capacity. What should you do?

A. Use the n1-highcpu-96 machine type in the configuration of the MIG.

B. Monitor results of Stackdriver Trace to determine the required amount of resources.

C. Validate that the resource requirements are within the available quota limits of each region.

D. Deploy the service in one region and use a global load balancer to route traffic to this region.

You have migrated an e-commerce application to Google Cloud Platform (GCP). You want to prepare the application for the upcoming busy season. What should you do first to prepare for the busy season?

A. Load teat the application to profile its performance for scaling.

B. Enable AutoScaling on the production clusters, in case there is growth.

C. Pre-provision double the compute power used last season, expecting growth.

D. Create a runbook on inflating the disaster recovery (DR) environment if there is growth.

You support a service with a well-defined Service Level Objective (SLO). Over the previous 6 months, your service has consistently met its SLO and customer satisfaction has been consistently high. Most of your service's operations tasks are automated and few repetitive tasks occur frequently. You want to optimize the balance between reliability and deployment velocity while following site reliability engineering best practices. What should you do? (Choose two.)

A. Make the service's SLO more strict.

B. Increase the service's deployment velocity and/or risk.

C. Shift engineering time to other services that need more reliability.

D. Get the product team to prioritize reliability work over new features.

E. Change the implementation of your Service Level Indicators (SLIs) to increase coverage.

You deployed an application into a large Standard Google Kubernetes Engine (GKE) cluster. The application is stateless and multiple pods run at the same time. Your application receives inconsistent traffic. You need to ensure that the user experience remains consistent regardless of changes in traffic and that the resource usage of the cluster is optimized.

What should you do?

A. Configure a cron job to scale the deployment on a schedule

B. Configure a Horizontal Pod Autoscaler.

C. Configure a Vertical Pod Autoscaler

D. Configure cluster autoscaling on the node pool.

You encounter a large number of outages in the production systems you support. You receive alerts for all the outages, the alerts are due to unhealthy systems that are automatically restarted within a minute. You want to set up a process that would prevent staff burnout while following Site Reliability Engineering (SRE) practices. What should you do?

A. Eliminate alerts that are not actionable

B. Redefine the related SLO so that the error budget is not exhausted

C. Distribute the alerts to engineers in different time zones

D. Create an incident report for each of the alerts

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 Kubernetes 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 (IaC) and application code?

A. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different overlay directories are different environments Application (app source code) repositories are separated: different branches are different features

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

C. Cloud Infrastructure (Terraform) repository is shared: different branches are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different overlay directories are different environments Application (app source code) repository is shared: different directories are different features

D. Cloud Infrastructure (Terraform) repositories are separated: different branches are different environments GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different overlay directories are different environments Application (app source code) repositories are separated: different branches are different

You are configuring a CI pipeline. The build step for your CI pipeline integration testing requires access to APIs inside your private VPC network. Your security team requires that you do not expose API traffic publicly. You need to implement a solution that minimizes management overhead. What should you do?

A. Use Cloud Build private pools to connect to the private VPC.

B. Use Spinnaker for Google Cloud to connect to the private VPC.

C. Use Cloud Build as a pipeline runner. Configure Internal HTTP(S) Load Balancing for API access.

D. Use Cloud Build as a pipeline runner. Configure External HTTP(S) Load Balancing with a Google Cloud Armor policy for API access.