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For this question, refer to the Dress4Win case study.

As part of Dress4Win's plans to migrate to the cloud, they want to be able to set up a managed logging and monitoring system so they can handle spikes in their traffic load. They want to ensure that:


· The infrastructure can be notified when it needs to scale up and down to handle the ebb and flow of usage throughout the day


· Their administrators are notified automatically when their application reports errors.
· They can filter their aggregated logs down in order to debug one piece of the application across many hosts

Which Google StackDriver features should they use?

  1. Logging, Alerts, Insights, Debug
  2. Monitoring, Trace, Debug, Logging
  3. Monitoring, Logging, Alerts, Error Reporting
  4. Monitoring, Logging, Debug, Error Report

Answer(s): D



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Dress4win has end to end tests covering 100% of their endpoints. They want to ensure that the move of cloud does not introduce any new bugs.
Which additional testing methods should the developers employ to prevent an outage?

  1. They should run the end to end tests in the cloud staging environment to determine if the code is working as intended.
  2. They should enable google stack driver debugger on the application code to show errors in the code
  3. They should add additional unit tests and production scale load tests on their cloud staging environment.
  4. They should add canary tests so developers can measure how much of an impact the new release causes to latency

Answer(s): B



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The current Dress4win system architecture has high latency to some customers because it is located in one data center.
As of a future evaluation and optimizing for performance in the cloud, Dresss4win wants to distribute it's system architecture to multiple locations when Google cloud platform.
Which approach should they use?

  1. Use regional managed instance groups and a global load balancer to increase performance because the regional managed instance group can grow instances in each region separately based on traffic.
  2. Use a global load balancer with a set of virtual machines that forward the requests to a closer group of virtual machines managed by your operations team.
  3. Use regional managed instance groups and a global load balancer to increase reliability by providing automatic failover between zones in different regions.
  4. Use a global load balancer with a set of virtual machines that forward the requests to a closer group of virtual machines as part of a separate managed instance groups.

Answer(s): A

Explanation:



View Related Case Study

For this question, refer to the Dress4Win case study. Dress4Win is expected to grow to 10 times its size in 1 year with a corresponding growth in data and traffic that mirrors the existing patterns of usage. The CIO has set the target of migrating production infrastructure to the cloud within the next 6 months. How will you configure the solution to scale for this growth without making major application changes and still maximize the ROI?

  1. Migrate the web application layer to App Engine, and MySQL to Cloud Datastore, and NAS to Cloud Storage. Deploy RabbitMQ, and deploy Hadoop servers using Deployment Manager.
  2. Migrate RabbitMQ to Cloud Pub/Sub, Hadoop to BigQuery, and NAS to Compute Engine with Persistent Disk storage. Deploy Tomcat, and deploy Nginx using Deployment Manager.
  3. Implement managed instance groups for Tomcat and Nginx. Migrate MySQL to Cloud SQL, RabbitMQ to Cloud Pub/Sub, Hadoop to Cloud Dataproc, and NAS to Compute Engine with Persistent Disk storage.
  4. Implement managed instance groups for the Tomcat and Nginx. Migrate MySQL to Cloud SQL, RabbitMQ to Cloud Pub/Sub, Hadoop to Cloud Dataproc, and NAS to Cloud Storage.

Answer(s): D






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