You are helping the QA team to roll out a new load-testing tool to test the scalability of your primary cloud services that run on Google Compute Engine with Cloud Bigtable.
Which three requirements should they include? Choose 3 answers.

• A.Schedule the load-testing tool to regularly run against the production environment
• B.Ensure all third-party systems your services use is capable of handling high load
• C.Create a separate Google Cloud project to use for the load-testing environment
• D.Instrument the production services to record every transaction for replay by the load-testing tool
• E.Instrument the load-testing tool and the target services with detailed logging and metrics collection
• F.Ensure that the load tests validate the performance of Cloud Bigtable

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Your company's test suite is a custom C++ application that runs tests throughout each day on Linux virtual machines. The full test suite takes several hours to complete, running on a limited number of on premises servers reserved for testing. Your company wants to move the testing infrastructure to the cloud, to reduce the amount of time it takes to fully test a change to the system, while changing the tests as little as possible. Which cloud infrastructure should you recommend?

• A.Google Compute Engine unmanaged instance groups and Network Load Balancer
• B.Google Compute Engine managed instance groups with auto-scaling
• C.Google Cloud Dataproc to run Apache Hadoop jobs to process each test
• D.Google App Engine with Google Stackdriver for logging

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Your customer is receiving reports that their recently updated Google App Engine application is taking approximately 30 seconds to load for some of their users. This behavior was not reported before the update. What strategy should you take?

• A.Work with your ISP to diagnose the problem.
• B.Open a support ticket to ask for network capture and flow data to diagnose the problem, then roll back your application.
• C.Roll back to an earlier known good release initially, then use Stackdriver Trace and logging to diagnose the problem in a development/test/staging environment.
• D.Roll back to an earlier known good release, then push the release again at a quieter period to investigate. Then use Stackdriver Trace and logging to diagnose the problem.

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You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading. Where should you store the data?

• A.Google BigQuery
• B.Google Cloud SQL
• C.Google Cloud Bigtable
• D.Google Cloud Storage

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The application reliability team at your company has added a debug feature to their backend service to send all server events to Google Cloud Storage for eventual analysis. The event records are at least 50 KB and at most 15 MB and are expected to peak at 3,000 events per second. You want to minimize data loss. Which process should you implement?

• A.Append metadata to file body.
  Compress individual files.
  Name files with serverName-Timestamp.
  Create a new bucket if bucket is older than 1 hour and save individual files to the new bucket.
  Otherwise, save files to existing bucket
• B.Batch every 10,000 events with a single manifest file for metadata.
  Compress event files and manifest file into a single archive file.
  Name files using serverName-EventSequence.
  Create a new bucket if bucket is older than 1 day and save the single archive file to the new bucket. Otherwise, save the single archive file to existing bucket.
• C.Compress individual files.
  Name files with serverName-EventSequence.
  Save files to one bucket
  Set custom metadata headers for each object after saving.
• D.Append metadata to file body.
  Compress individual files.
  Name files with a random prefix pattern.
  Save files to one bucket

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