Mountkirk Games wants to set up a continuous delivery pipeline. Their architecture includes many small
services that they want to be able to update and roll back quickly. Mountkirk Games has the following
requirements:
Services are deployed redundantly across multiple regions in the US and Europe

Only frontend services are exposed on the public internet

They can provide a single frontend IP for their fleet of services

Deployment artifacts are immutable

Which set of products should they use?

• A.Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine
• B.Google Cloud Storage, Google App Engine, Google Network Load Balancer
• C.Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer
• D.Google Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager

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You have an application deployed on Google Kubernetes Engine using a Deployment named echo-deployment.
The deployment is exposed using a Service called echo-service. You need to perform an update to the application with minimal downtime to the application. What should you do?

• A.Use kubectl set image deployment/echo-deployment <new-image>
• B.Use the rolling update functionality of the Instance Group behind the Kubernetes cluster
• C.Update the deployment yaml file with the new container image. Use kubectl delete deployment/ echo-deployment and kubectl create -f <yaml-file>
• D.Update the service yaml file which the new container image. Use kubectl delete service/echo- service and kubectl create -f <yaml-file>

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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 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 version number.
  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.
• 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-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.
• D.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.

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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 data.
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 job

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For this question, refer to the Dress4Win case study. You are responsible for the security of data stored in Cloud Storage for your company, Dress4Win. You have already created a set of Google Groups and assigned the appropriate users to those groups. You should use Google best practices and implement the simplest design to meet the requirements.
Considering Dress4Win's business and technical requirements, what should you do?

• A.Assign custom IAM roles to the Google Groups you created in order to enforce security requirements.
  Encrypt data with a customer-supplied encryption key when storing files in Cloud Storage.
• B.Assign custom IAM roles to the Google Groups you created in order to enforce security requirements.
  Enable default storage encryption before storing files in Cloud Storage.
• C.Assign predefined IAM roles to the Google Groups you created in order to enforce security requirements.
  Utilize Google's default encryption at rest when storing files in Cloud Storage.
• D.Assign predefined IAM roles to the Google Groups you created in order to enforce security requirements. Ensure that the default Cloud KMS key is set before storing files in Cloud Storage.

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