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

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Your team is designing a new application for deployment into Google Kubernetes Engine (GKE). You need to set up monitoring to collect and aggregate various application-level metrics in a centralized location. You want to use Google Cloud Platform services while minimizing the amount of work required to set up monitoring. What should you do?

• A.Emit all metrics in the form of application-specific log messages, pass these messages from the containers to the Stackdriver logging collector, and then observe metrics in Stackdriver.
• B.Publish various melrics from the application directly to the Slackdriver Monitoring API, and then observe these custom metrics in Stackdriver.
• C.Install the Cloud Pub/Sub client libraries, push various metrics from the application to various topics, and then observe the aggregated metrics in Stackdriver.
• D.Install the OpenTelemetry client libraries in the application, configure Stackdriver as the export destination for the metrics, and then observe the application's metrics in Stackdriver.

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You are part of an organization that follows SRE practices and principles. You are taking over the management of a new service from the Development Team, and you conduct a Production Readiness Review (PRR). After the PRR analysis phase, you determine that the service cannot currently meet its Service Level Objectives (SLOs). You want to ensure that the service can meet its SLOs in production. What should you do next?

• A.Bring the service into production with no SLOs and build them when you have collected operational data.
• B.djust the SLO targets to be achievable by the service so you can bring it into production.
• C.Notify the development team that they will have to provide production support for the service.
• D.Identify recommended reliability improvements to the service to be completed before handover.

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You support an application running on App Engine. The application is used globally and accessed from various device types. You want to know the number of connections. You are using Stackdriver Monitoring for App Engine. What metric should you use?

• A.flex/connections/current
• B.tcp_ssl_proxy/new_connections
• C.(lex/jnstance/connections/current
• D.tcp_ssl_proxy/open_connections

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You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline. Your application has an in-memory cache that loads objects at start time. You want to automate the comparison of the canary version against the production version. How should you configure the canary analysis?

• A.Compare the canary with a new deployment of the previous production version.
• B.Compare the canary with a new deployment of the current production version.
• C.Compare the canary with the existing deployment of the current production version.
• D.Compare the canary with the average performance of a sliding window of previous production versions.

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