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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 Cloud Build to create a Compute Engine instance in the private VPC. Run the integration tests on the VM by using a startup script.

C.

Use Cloud Build as a pipeline runner. Configure a cross-region internal Application Load Balancer for API access.

D.

Use Cloud Build as a pipeline runner. Configure a global external Application Load Balancer with a Google Cloud Armor policy for API access.

Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to a Kubernetes cluster in the production environment. The security auditor is concerned that developers or operators could circumvent automated testing and push code changes to production without approval. What should you do to enforce approvals?

A.

Configure the build system with protected branches that require pull request approval.

B.

Use an Admission Controller to verify that incoming requests originate from approved sources.

C.

Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.

D.

Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.

You are running an application in a virtual machine (VM) using a custom Debian image. The image has the Stackdriver Logging agent installed. The VM has the cloud-platform scope. The application is logging information via syslog. You want to use Stackdriver Logging in the Google Cloud Platform Console to visualize the logs. You notice that syslog is not showing up in the "All logs" dropdown list of the Logs Viewer. What is the first thing you should do?

A.

Look for the agent's test log entry in the Logs Viewer.

B.

Install the most recent version of the Stackdriver agent.

C.

Verify the VM service account access scope includes the monitoring.write scope.

D.

SSH to the VM and execute the following commands on your VM: ps ax I grep fluentd

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?

A.

Enable VPC Service Controls for the container.googleapis.com API service.

B.

Revoke the resourcemanager.projects.updateLiens permission from all users associated with the project.

C.

Enable the compute.restrictXpnProjectLienRemoval organization policy constraint.

D.

Instruct teams to only perform IAM permission management as code with Terraform.

You are designing a new Google Cloud organization for a client. Your client is concerned with the risks associated with long-lived credentials created in Google Cloud. You need to design a solution to completely eliminate the risks associated with the use of JSON service account keys while minimizing operational overhead. What should you do?

A.

Use custom versions of predefined roles to exclude all iam.serviceAccountKeys. * service account role permissions.

B.

Apply the constraints/iam.disableserviceAccountKeycreation constraint to the organization.

C.

Apply the constraints/iam. disableServiceAccountKeyUp10ad constraint to the organization.

D.

Grant the roles/ iam.serviceAccountKeyAdmin IAM role to organization administrators only.

The new version of your containerized application has been tested and is ready to be deployed to production on Google Kubernetes Engine (GKE) You could not fully load-test the new version in your pre-production environment and you need to ensure that the application does not have performance problems after deployment Your deployment must be automated What should you do?

A.

Deploy the application through a continuous delivery pipeline by using canary deployments Use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics

B.

Deploy the application through a continuous delivery pipeline by using blue/green deployments Migrate traffic to the new version of the application and use Cloud Monitoring to look for performance issues

C.

Deploy the application by using kubectl and use Config Connector to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues

D.

Deploy the application by using kubectl and set the spec. updatestrategy. type field to RollingUpdate Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.

Your application services run in Google Kubernetes Engine (GKE). You want to make sure that only images from your centrally-managed Google Container Registry (GCR) image registry in the altostrat-images project can be deployed to the cluster while minimizing development time. What should you do?

A.

Create a custom builder for Cloud Build that will only push images to gcr.io/altostrat-images.

B.

Use a Binary Authorization policy that includes the whitelist name pattern gcr.io/attostrat-images/.

C.

Add logic to the deployment pipeline to check that all manifests contain only images from gcr.io/altostrat-images.

D.

Add a tag to each image in gcr.io/altostrat-images and check that this tag is present when the image is deployed.

Your company runs services by using multiple globally distributed Google Kubernetes Engine (GKE) clusters Your operations team has set up workload monitoring that uses Prometheus-based tooling for metrics alerts: and generating dashboards This setup does not provide a method to view metrics globally across all clusters You need to implement a scalable solution to support global Prometheus querying and minimize management overhead What should you do?

A.

Configure Prometheus cross-service federation for centralized data access

B.

Configure workload metrics within Cloud Operations for GKE

C.

Configure Prometheus hierarchical federation for centralized data access

D.

Configure Google Cloud Managed Service for Prometheus

You are developing the deployment and testing strategies for your CI/CD pipeline in Google Cloud You must be able to

• Reduce the complexity of release deployments and minimize the duration of deployment rollbacks

• Test real production traffic with a gradual increase in the number of affected users

You want to select a deployment and testing strategy that meets your requirements What should you do?

A.

Recreate deployment and canary testing

B.

Blue/green deployment and canary testing

C.

Rolling update deployment and A/B testing

D.

Rolling update deployment and shadow testing

Your company is developing applications that are deployed on Google Kubernetes Engine (GKE) Each team manages a different application You need to create the development and production environments for each team while you minimize costs Different teams should not be able to access other teams environments You want to follow Google-recommended practices What should you do?

A.

Create one Google Cloud project per team In each project create a cluster for development and one forproduction Grant the teams Identity and Access Management (1AM) access to their respective clusters

B.

Create one Google Cloud project per team In each project create a cluster with a Kubernetes namespacefor development and one for production Grant the teams Identity and Access Management (1AM) access to their respective clusters.

C.

Create a development and a production GKE cluster in separate projects In each cluster create a Kubernetes namespace per team and then configure Identity-Aware Proxy so that each team can onlyaccess its own namespace

D.

Create a development and a production GKE cluster in separate projects In each cluster create a Kubernetes namespace per team and then configure Kubernetes role-based access control (RBAC) so that each team can only access its own namespace