You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take?

• A.Perform the following:
  1) Create a managed instance group with f1-micro type machines.
  2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
  3) Restart the instances to automatically deploy new production releases.
• B.Perform the following:
  1) Create a managed instance group with n1-standard-1 type machines.
  2) Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.
  3) Rebuild the Compute Engine image, and update the instance template to deploy new production releases.
• C.Perform the following:
  1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
  2) Build a Docker image from the production branch with all of the dependencies, and tag it with the
  3) Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.
• D.Perform the following:
  1) Create a Kubernetes Engine cluster with n1-standard-4 type machines.
  2) Build a Docker image from the master branch will all of the dependencies, and tag it with "latest".
  3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to "Always".
  Restart the pods to automatically deploy new production releases.

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You have an application that makes HTTP requests to Cloud Storage. Occasionally the requests fail with HTTP status codes of 5xx and 429.
How should you handle these types of errors?

• A.Use gRPC instead of HTTP for better performance.
• B.Implement retry logic using a truncated exponential backoff strategy.
• C.Make sure the Cloud Storage bucket is multi-regional for geo-redundancy.
• D.Monitor https://status.cloud.google.com/feed.atom and only make requests if Cloud Storage is not reporting an incident.

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You are designing a large distributed application with 30 microservices. Each of your distributed microservices needs to connect to a database back-end. You want to store the credentials securely. Where should you store the credentials?

• A.In an environment variable
• B.In a secret management system
• C.In the source code
• D.In a config file that has restricted access through ACLs

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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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For this question, refer to the TerramEarth case study.
TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the dat a. What is the most cost-effective way to run this job?

• A.Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
• B.Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.
• C.Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.
• D.Launch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the jo

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