Free Google-Professional-Cloud-DevOps-Engineer – Professional Cloud DevOps Engineer Practice Test Question

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  Your application servers' logs.

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  Instrumentation coded directly in the client.

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  Metrics exported from the application servers.

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  GKE health checks for your application servers.

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  A synthetic client that periodically sends simulated user requests.
  
  

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  Make the service’s SLO more strict.

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  Increase the service’s deployment velocity and/or risk.

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  Shift engineering time to other services that need more reliability.

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  Get the product team to prioritize reliability work over new features.

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  Change the implementation of your Service Level Indicators (SLIs) to increase coverage.
  
  

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  Review current application metrics and add new ones as needed.

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  Modify the code to capture additional information for user interaction.

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  Analyze the web proxy logs only and capture response time of each request.

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  Create new synthetic clients to simulate a user journey using the application.
  

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  Use current and historic Request Logs to trace customer interaction with the application.
  
  

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  Before merging new code, require 2 different peers to review the code changes.

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  Adopt the blue/green deployment strategy when releasing new code via a CD server.

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  Integrate a code linting tool to validate coding standards before any code is accepted into the repository.

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  Require developers to run automated integration tests on their local development environments before release.

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  Configure a CI server. Add a suite of unit tests to your code and have your CI server run them on commit and verify any changes.
  
  

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  Operations Lead

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  Engineering Lead

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  Communications Lead

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  Customer Impact Assessor

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  External Customer Communications Lead
  
  

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  Disable the CI pipeline and revert to manually building and pushing the artifacts.

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  Change the CI pipeline to push the artifacts is Container Registry instead of Docker Hub.

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  Upload the configuration YAML file to Cloud Storage and use Error Reporting to identify and fix the issue.

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  Run a Git compare between the previous and current Cloud Build Configuration files to find and fix the bug.
  
  

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  Use Stackdriver Kubernetes Engine Monitoring.

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  Use Prometheus to collect and aggregate logs per container, and then analyze the results in Grafana.

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  Use the Stackdriver Monitoring API to create custom metrics, and then organize your containers using groups.

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  Use Stackdriver Logging to export application logs to BigQuery, aggregate logs per container, and then analyze CPU and memory consumption.
  
  

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  Store the encryption keys in Cloud Key Management Service (KMS) and rotate the keys frequently

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  Inject the secret at the time of instance creation via an encrypted configuration management system.

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  Integrate the application with a Single sign-on (SSO) system and do not expose secrets to the application.

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  Leverage a continuous build pipeline that produces multiple versions of the secret for each instance of the application.
  
  

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  Create a Cloud Storage bucket and develop your application to send logs directly to the bucket.

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  Develop an App Engine application that pulls the logs from Stackdriver and saves them in BigQuery.

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  Create an export in Stackdriver and configure Cloud Pub/Sub to store logs in permanent storage for seven years.

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  Create a sink in Stackdriver, name it, create a bucket on Cloud Storage for storing archived logs, and then select the bucket as the log export destination.
  
  

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  90th percentile – 100ms
  95th percentile – 250ms

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  90th percentile – 120ms
  95th percentile – 275ms

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  90th percentile – 150ms
  95th percentile – 300ms

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  90th percentile – 250ms
  95th percentile – 400ms