Professional-Cloud-DevOps-Engineer Exam Dumps – Our Features

Your company follows Site Reliability Engineering principles. You are writing a postmortem for an incident, triggered by a software change, that severely affected users. You want to prevent severe incidents from happening in the future. What should you do?

• Identify engineers responsible for the incident and escalate to their senior management.
• Ensure that test cases that catch errors of this type are run successfully before new software releases.
• Follow up with the employees who reviewed the changes and prescribe practices they should follow in the future.
• Design a policy that will require on-call teams to immediately call engineers and management to discuss a plan of action if an incident occurs.

Your team of Infrastructure DevOps Engineers is growing, and you are starting to use Terraform to manage infrastructure. You need a way to implement code versioning and to share code with other team members. What should you do?

• Store the Terraform code in a version-control system. Establish procedures for pushing new versions and merging with the master.
• Store the Terraform code in a network shared folder with child folders for each version release. Ensure that everyone works on different files.
• Store the Terraform code in a Cloud Storage bucket using object versioning. Give access to the bucket to every team member so they can download the files.
• Store the Terraform code in a shared Google Drive folder so it syncs automatically to every team member’s computer. Organize files with a naming convention that identifies each new version.

Your team is building a service that performs compute-heavy processing on batches of data The data is processed faster based on the speed and number of CPUs on the machine These batches of data vary in size and may arrive at any time from multiple third-party sources You need to ensure that third parties are able to upload their data securely. You want to minimize costs while ensuring that the data is processed as quickly as possible What should you do?

• Provide a secure file transfer protocol (SFTP) server on a Compute Engine instance so that third parties can upload batches of data and provide appropriate credentials to the server
• Create a Cloud Function with a google.storage, object, finalize Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group
• Use an image pre-loaded with the data processing software that terminates the instances when processing completes
• Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (1AM) access to the bucket
• Use a standard Google Kubernetes Engine (GKE) cluster and maintain two services one that
• processes the batches of data and one that monitors Cloud Storage for new batches of data
• Stop the processing service when there are no batches of data to process C.
• Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate identity and Access Management (1AM) access to the bucket
• Create a Cloud Function with a google, storage, object .finalise Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group
• Use an image pre-loaded with the data processing software that terminates the instances when processing completes
• Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (1AM) access to the bucket
• Use Cloud Monitoring to detect new batches of data in the bucket and trigger a Cloud Function that processes the data
• Set a Cloud Function to use the largest CPU possible to minimize the runtime of the processing

Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, and an application written in GO. You noticed that the performance inconsistently degrades at peak load. You could not reproduce this issue on your workstation. You need to continuously monitor the application in production to identify slow paths in the code. You want to minimize performance impact and management overhead. What should you do?

• Install a continuous profiling tool into Compute Engine. Configure the application to send profiling data to the tool.
• Periodically run the go tool pprof command against the application instance. Analyze the results by using flame graphs.
• Configure Cloud Profiler, and initialize the [email protected]/go/profiler library in the application.
• Use Cloud Monitoring to assess the App Engine CPU utilization metric.

Your company has a Google Cloud resource hierarchy with folders for production test and development Your cyber security team needs to review your company's Google Cloud security posture to accelerate security issue identification and resolution You need to centralize the logs generated by Google Cloud services from all projects only inside your production folder to allow for alerting and near-real time analysis. What should you do?

• Enable the Workflows API and route all the logs to Cloud Logging
• Create a central Cloud Monitoring workspace and attach all related projects
• Create an aggregated log sink associated with the production folder that uses a Pub Sub topic as the destination
• Create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination

You are running a real-time gaming application on Compute Engine that has a production and testing environment. Each environment has their own Virtual Private Cloud (VPC) network. The application frontend and backend servers are located on different subnets in the environment's VPC. You suspect there is a malicious process communicating intermittently in your production frontend servers. You want to ensure that network traffic is captured for analysis. What should you do?

• Enable VPC Flow Logs on the production VPC network frontend and backend subnets only with a sample volume scale of 0.5.
• Enable VPC Flow Logs on the production VPC network frontend and backend subnets only with a sample volume scale of 1.0.
• Enable VPC Flow Logs on the testing and production VPC network frontend and backend subnets with a volume scale of 0.5. Apply changes intesting before production.
• Enable VPC Flow Logs on the testing and production VPC network frontend and backend subnets with a volume scale of 1.0. Apply changes in testing before production.

You are configuring a Cl pipeline. The build step for your Cl 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?

• Use Cloud Build private pools to connect to the private VPC.
• Use Spinnaker for Google Cloud to connect to the private VPC.
• Use Cloud Build as a pipeline runner. Configure Internal HTTP(S) Load Balancing for API access.
• Use Cloud Build as a pipeline runner. Configure External HTTP(S) Load Balancing with a Google Cloud Armor policy for API access.

Some of your production services are running in Google Kubernetes Engine (GKE) in the eu-west-1 region. Your build system runs in the us-west-1 region. You want to push the container images from your build system to a scalable registry to maximize the bandwidth for transferring the images to the cluster. What should you do?

• Push the images to Google Container Registry (GCR) using the gcr.io hostname.
• Push the images to Google Container Registry (GCR) using the us.gcr.io hostname.
• Push the images to Google Container Registry (GCR) using the eu.gcr.io hostname.
• Push the images to a private image registry running on a Compute Engine instance in the eu-west-1 region.

Your company runs an ecommerce website built with JVM-based applications and microservice architecture in Google Kubernetes Engine (GKE) The application load increases during the day and decreases during the night Your operations team has configured the application to run enough Pods to handle the evening peak load You want to automate scaling by only running enough Pods and nodes for the load What should you do?

• Configure the Vertical Pod Autoscaler but keep the node pool size static
• Configure the Vertical Pod Autoscaler and enable the cluster autoscaler
• Configure the Horizontal Pod Autoscaler but keep the node pool size static
• Configure the Horizontal Pod Autoscaler and enable the cluster autoscaler

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 Kubemetes Engine (GKE) clustercreated so that application teams can deploy their applications. Anthos Config Management will be used and templated to deployinfrastructure 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 (laC) and application code?

• Cloud Infrastructure (Terraform) repository is shared: different directories are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different overlay directories are different environmentsApplication (app source code) repositories are separated: different branches are different features
• Cloud Infrastructure (Terraform) repository is shared: different directories are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different branches are different environmentsApplication (app source code) repositories are separated: different branches are different features
• Cloud Infrastructure (Terraform) repository is shared: different branches are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: different overlay directories are different environmentsApplication (app source code) repository is shared: different directories are different features
• 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 features