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 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.
• D.Perform the following:
  1) Create a Kubernetes Engine (GKE) 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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Case Study: 11 - TerramEarth
Company overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries.
Their mission is to build products that make their customers more productive.
Solution concept
There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth.
Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base.
Each vehicle usually generates 200 to 500 megabytes of data per day.
Existing technical environment
TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics.
Business requirements
- Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible.
- Decrease cloud operational costs and adapt to seasonality.
- Increase speed and reliability of development workflow.
- Allow remote developers to be productive without compromising code or data security.
- Create a flexible and scalable platform for developers to create custom API services for dealers and partners.
Technical requirements
- Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations.
- Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments.
- Allow developers to run experiments without compromising security and governance requirements.
- Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints.
- Use cloud-native solutions for keys and secrets management and optimize for identity-based access.
- Improve and standardize tools necessary for application and network monitoring and troubleshooting.
Executive statement
Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes.
After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in- class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.
You start to build a new application that uses a few Cloud Functions for the backend. One use case requires a Cloud Function func_display to invoke another Cloud Function func_query. You want func_query only to accept invocations from func_display. You also want to follow Google's recommended best practices. What should you do?

• A.Create a token and pass it in as an environment variable to func_display. When invoking func_query, include the token in the request. Pass the same token to func_query and reject the invocation if the tokens are different.
• B.Make func_query 'Require authentication.' Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.
• C.Make func_query 'Require authentication' and only accept internal traffic. Create those two functions in the same VPC. Create an ingress firewall rule for func_query to only allow traffic from func_display.
• D.Create those two functions in the same project and VPC. Make func_query only accept internal traffic. Create an ingress firewall for func_query to only allow traffic from func_display. Also, make sure both functions use the same service account.

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You are designing a hybrid cloud network for a financial services company with two on-premises data centers in different geographical regions. The company requires a highly available (99.9% SLA) and low-latency connection to a single VPC in Google Cloud for business-critical, high-throughput applications. You need to design the most resilient connectivity solution to protect against a regional on-premises failure. What should you do?

• A.Provision an HA VPN connection from each on-premises data center to the VPC.
• B.Provision two Dedicated Interconnect connections from each on-premises data center to the VPC.
• C.Deploy an IPsec VPN on Compute Engine in the VPC network. Configure connectivity from each on- premises data center to the VPN.
• D.Provision a single Dedicated Interconnect connection to the VPC.

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You created a Cloud Monitoring dashboard for your dev environment to view real-time application metrics The Ops team wants a similar dashboard to monitor the production environment. You want to minimize time and effort as you assist the Ops team with their dashboard request What should you do?

• A.Export your dashboard to a JSON file and share it with the Ops team.
• B.Create a shared document and list every step you performed lo create the dashboard. The Ops team can use the document to build their own dashboard.
• C.Grant viewer permission to the Ops team in your project so they can recreate the charts in their dashboard from the console.
• D.Share your dashboard and send the shared dashboard URL to the Ops team

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For this question, refer to the TerramEarth case study. You need to implement a reliable, scalable GCP solution for the data warehouse for your company, TerramEarth. Considering the TerramEarth business and technical requirements, what should you do?

• A.Replace the existing data warehouse with BigQuery. Use table partitioning.
• B.Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.
• C.Replace the existing data warehouse with BigQuery. Use federated data sources.
• D.Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine pre-emptible instance with 32 CPUs.

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