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Free PROFESSIONAL-CLOUD-DEVOPS-ENGINEER Exam Braindumps (page: 22)

Page 21 of 46
PDF with 201 Questions

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.)

  1. Make the service's SLO more strict.
  2. Increase the service's deployment velocity and/or risk.
  3. Shift engineering time to other services that need more reliability.
  4. Get the product team to prioritize reliability work over new features.
  5. Change the implementation of your Service Level Indicators (SLIs) to increase coverage.

Answer(s): B,C

Explanation:

(https://sre.google/workbook/implementing-slos/#slo-decision-matrix)



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 2 answers

  1. Replace the CAB with a senior manager to ensure continuous oversight from development to deployment
  2. Let developers merge their own changes but ensure that the team's deployment platform can roll back changes if any issues are discovered
  3. Move to a peer-review based process for individual changes that is enforced at code check-in time and supported by automated tests
  4. Batch changes into larger but less frequent software releases
  5. Ensure that the team's development platform enables developers to get fast feedback on the impact of their changes

Answer(s): C,E

Explanation:

A change advisory board (CAB) is a traditional way of approving changes to a service, but it can slow down the software delivery performance and introduce bottlenecks. A better way to improve the speed and quality of changes is to use a peer-review based process for individual changes that is enforced at code check-in time and supported by automated tests. This way, developers can get fast feedback on the impact of their changes and catch any errors or bugs before they reach production. Additionally, the team's development platform should enable developers to get fast feedback on the impact of their changes, such as using Cloud Code, Cloud Build, or Cloud Debugger.



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?

  1. 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
  2. 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
  3. 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.
  4. Deploy the canary release to Google Kubernetes Engine with Anthos Sen/ice 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

Answer(s): D

Explanation:

The best option for deploying a containerized web application to Google Cloud with the given acceptance criteria is to use Google Kubernetes Engine (GKE) with Anthos Service Mesh. GKE is a managed service for running Kubernetes clusters on Google Cloud, and Anthos Service Mesh is a service mesh that provides observability, traffic management, and security features for microservices. With Anthos Service Mesh, you can use traffic splitting to direct traffic from Android devices to an Android-specific microservice by configuring the user-agent header in the virtual service. You can also use traffic splitting to direct arbitrary percentage-based traffic to different versions of any microservice for continuous testing. For example, you can use a canary release strategy to direct 10% of user traffic to a new version of a microservice and monitor its performance and reliability.



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?

  1. Create SLIs from metrics Enable Alert Policies if the services do not pass
  2. Use the metrics from Anthos Service Mesh to measure the health of the microservices
  3. Create a SLO Create an Alert Policy on select_slo_bum_rate
  4. Create a SLO and configure uptime checks for your services Enable Alert Policies if the services do not pass

Answer(s): C

Explanation:

The best option for detecting consumption of an error budget to protect customers and define release policies is to create a service level objective (SLO) and create an alert policy on select_slo_burn_rate. A SLO is a target value or range of values for a service level indicator (SLI) that measures some aspect of the service quality, such as availability or latency. An error budget is the amount of time or number of errors that a service can tolerate while still meeting its SLO. A select_slo_burn_rate is a metric that indicates how fast the error budget is being consumed by the service. By creating an alert policy on select_slo_burn_rate, you can trigger notifications or actions when the error budget consumption exceeds a certain threshold. This way, you can balance change, velocity, and reliability of the service by adjusting the release policies based on the error budget status.






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