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.
Your organization uses a change advisory board (CAB) to approve all changes to an existing service. You want to revise this process to eliminate any negative impact on the software delivery performance. What should you do? (Choose two.)
A. Replace the CAB with a senior manager to ensure continuous oversight from development to deployment.
B. Let developers merge their own changes, but ensure that the team's deployment platform can roll back changes if any issues are discovered.
C. Move to a peer-review based process for individual changes that is enforced at code check-in time and supported by automated tests.
D. Batch changes into larger but less frequent software releases.
E. Ensure that the team's development platform enables developers to get fast feedback on the impact of their changes.
Your organization has a containerized web application that runs on-premises. As part of the migration plan to Google Cloud, you need to select a deployment strategy and platform that meets the following acceptance criteria:
1.
The platform must be able to direct traffic from Android devices to an Android-specific microservice.
2.
The platform must allow for arbitrary percentage-based traffic splitting
3.
The deployment strategy must allow for continuous testing of multiple versions of any microservice.
What should you do?
A. Deploy the canary release of the application to Cloud Run. Use traffic splitting to direct 10% of user traffic to the canary release based on the revision tag.
B. Deploy the canary release of the application to App Engine. Use traffic splitting to direct a subset of user traffic to the new version based on the IP address.
C. Deploy the canary release of the application to Compute Engine. Use Anthos Service Mesh with Compute Engine to direct 10% of user traffic to the canary release by configuring the virtual service.
D. Deploy the canary release to Google Kubernetes Engine with Anthos Service Mesh. Use traffic splitting to direct 10% of user traffic to the new version based on the user-agent header configured in the virtual service.
Your team is running microservices in Google Kubernetes Engine (GKE). You want to detect consumption of an error budget to protect customers and define release policies. What should you do?
A. Create SLIs from metrics. Enable Alert Policies if the services do not pass.
B. Use the metrics from Anthos Service Mesh to measure the health of the microservices.
C. Create a SLO. Create an Alert Policy on select_slo_burn_rate.
D. Create a SLO and configure uptime checks for your services. Enable Alert Policies if the services do not pass.
Your organization wants to collect system logs that will be used to generate dashboards in Cloud Operations for their Google Cloud project. You need to configure all current and future Compute Engine instances to collect the system logs, and you must ensure that the Ops Agent remains up to date. What should you do?
A. Use the gcloud CLI to install the Ops Agent on each VM listed in the Cloud Asset Inventory,
B. Select all VMs with an Agent status of Not detected on the Cloud Operations VMs dashboard. Then select Install agents.
C. Use the gcloud CLI to create an Agent Policy.
D. Install the Ops Agent on the Compute Engine image by using a startup script
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?
A. Enable the Workflows API and route all the logs to Cloud Logging.
B. Create a central Cloud Monitoring workspace and attach all related projects.
C. Create an aggregated log sink associated with the production folder that uses a Pub/Sub topic as the destination.
D. Create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination.
You are configuring the frontend tier of an application deployed in Google Cloud. The frontend tier is hosted in nginx and deployed using a managed instance group with an Envoy-based external HTTP(S) load balancer in front. The application is deployed entirely within the europe-west2 region, and only serves users based in the United Kingdom. You need to choose the most cost-effective network tier and load balancing configuration. What should you use?
A. Premium Tier with a global load balancer
B. Premium Tier with a regional load balancer
C. Standard Tier with a global load balancer
D. Standard Tier with a regional load balancer
You recently deployed your application in Google Kubernetes Engine (GKE) and now need to release a new version of the application. You need the ability to instantly roll back to the previous version of the application in case there are issues with the new version. Which deployment model should you use?
A. Perform a rolling deployment, and test your new application after the deployment is complete.
B. Perform A/B testing, and test your application periodically after the deployment is complete.
C. Perform a canary deployment, and test your new application periodically after the new version is deployed.
D. Perform a blue/green deployment, and test your new application after the deployment is complete.
You are building and deploying a microservice on Cloud Run for your organization. Your service is used by many applications internally. You are deploying a new release, and you need to test the new version extensively in the staging and production environments. You must minimize user and developer impact. What should you do?
A. Deploy the new version of the service to the staging environment. Split the traffic, and allow 1% of traffic through to the latest version. Test the latest version. If the test passes, gradually roll out the latest version to the staging and production environments.
B. Deploy the new version of the service to the staging environment. Split the traffic, and allow 50% of traffic through to the latest version. Test the latest version. If the test passes, send all traffic to the latest version. Repeat for the production environment.
C. Deploy the new version of the service to the staging environment with a new-release tag without serving traffic. Test the new-release version. If the test passes, gradually roll out this tagged version. Repeat for the production environment.
D. Deploy a new environment with the green tag to use as the staging environment. Deploy the new version of the service to the green environment and test the new version. If the tests pass, send all traffic to the green environment and delete the existing staging environment. Repeat for the production environment.
You work for a global organization and run a service with an availability target of 99% with limited engineering resources.
For the current calendar month, you noticed that the service has 99.5% availability. You must ensure that your service meets the defined availability goals and can react to business changes, including the upcoming launch of new features.
You also need to reduce technical debt while minimizing operational costs. You want to follow Google-recommended practices. What should you do?
A. Add N+1 redundancy to your service by adding additional compute resources to the service.
B. Identify, measure, and eliminate toil by automating repetitive tasks.
C. Define an error budget for your service level availability and minimize the remaining error budget.
D. Allocate available engineers to the feature backlog while you ensure that the service remains within the availability target.
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