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?

• A.Configure the Vertical Pod Autoscaler but keep the node pool size static
• B.Configure the Vertical Pod Autoscaler and enable the cluster autoscaler
• C.Configure the Horizontal Pod Autoscaler but keep the node pool size static
• D.Configure the Horizontal Pod Autoscaler and enable the cluster autoscaler

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Your application images are built using Cloud Build and pushed to Google Container Registry (GCR). You want to be able to specify a particular version of your application for deployment based on the release version tagged in source control. What should you do when you push the image?

• A.Reference the image digest in the source control tag.
• B.Supply the source control tag as a parameter within the image name.
• C.Use Cloud Build to include the release version tag in the application image.
• D.Use GCR digest versioning to match the image to the tag in source control.

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You support a large service with a well-defined Service Level Objective (SLO). The development team deploys new releases of the service multiple times a week. If a major incident causes the service to miss its SLO, you want the development team to shift its focus from working on features to improving service reliability. What should you do before a major incident occurs?

• A.Develop an appropriate error budget policy in cooperation with all service stakeholders.
• B.Negotiate with the product team to always prioritize service reliability over releasing new features.
• C.Negotiate with the development team to reduce the release frequency to no more than once a week.
• D.Add a plugin to your Jenkins pipeline that prevents new releases whenever your service is out of SLO.

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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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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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