You currently store the virtual machine (VM) utilization logs in Stackdriver. You need to provide an easy-to- share interactive VM utilization dashboard that is updated in real time and contains information aggregated on a quarterly basis. You want to use Google Cloud Platform solutions. What should you do?

• A.1. Export VM utilization logs from Stackdriver to BigQuery.
  2. From BigQuery, export the logs to a CSV file.
  3. Import the CSV file into Google Sheets.
  4. Build a dashboard in Google Sheets and share it with your stakeholders.
• B.1. Export VM utilization logs from Stackdriver to BigQuery.
  2. Create a dashboard in Data Studio.
  3. Share the dashboard with your stakeholders.
• C.1. Export VM utilization logs from Stackdriver to Cloud Pub/Sub.
  2. From Cloud Pub/Sub, send the logs to a Security Information and Event Management (SIEM) system.
  3. Build the dashboards in the SIEM system and share with your stakeholders.
• D.1. Export VM utilization logs from Stackdriver to a Cloud Storage bucket.
  2. Enable the Cloud Storage API to pull the logs programmatically.
  3. Build a custom data visualization application.
  4. Display the pulled logs in a custom dashboard.

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You have a CI/CD pipeline that uses Cloud Build to build new Docker images and push them to Docker Hub.
You use Git for code versioning. After making a change in the Cloud Build YAML configuration, you notice that no new artifacts are being built by the pipeline. You need to resolve the issue following Site Reliability Engineering practices. What should you do?

• A.Disable the CI pipeline and revert to manually building and pushing the artifacts.
• B.Upload the configuration YAML file to Cloud Storage and use Error Reporting to identify and fix the issue.
• C.Run a Git compare between the previous and current Cloud Build Configuration files to find and fix the bug.
• D.Change the CI pipeline to push the artifacts is Container Registry instead of Docker Hub.

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You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?

• A.Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.
• B.Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
• C.Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.
• D.Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.

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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.Monitor results of Stackdriver Trace to determine the required amount of resources.
• B.Deploy the service in one region and use a global load balancer to route traffic to this region.
• C.Use the n1-highcpu-96 machine type in the configuration of the MIG.
• D.Validate that the resource requirements are within the available quota limits of each region.

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Your product is currently deployed in three Google Cloud Platform (GCP) zones with your users divided between the zones. You can fail over from one zone to another, but it causes a 10-minute service disruption for the affected users. You typically experience a database failure once per quarter and can detect it within five minutes. You are cataloging the reliability risks of a new real-time chat feature for your product. You catalog the following information for each risk:
* Mean Time to Detect (MUD} in minutes
* Mean Time to Repair (MTTR) in minutes
* Mean Time Between Failure (MTBF) in days
* User Impact Percentage
The chat feature requires a new database system that takes twice as long to successfully fail over between zones. You want to account for the risk of the new database failing in one zone. What would be the values for the risk of database failover with the new system?

• A.MTTD: 5
  MTTR: 10
  MTBF: 90
  Impact: 33%
• B.MTTD:5
  MTTR: 20
  MTBF: 90
  Impact: 33%
• C.MTTD:5
  MTTR: 10
  MTBF: 90
  Impact 50%
• D.MTTD:5
  MTTR: 20
  MTBF: 90
  Impact: 50%

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