Google Prof Cloud Archi - pt 8

Mountkirk Games wants you to design their new testing strategy. How should the test coverage differ from their existing backends on the other platforms?

• Tests should scale well beyond the prior approaches

• Unit tests are no longer required, only end-to-end tests

• Tests should be applied after the release is in the production environment

• Tests should include directly testing the Google Cloud Platform (GCP) infrastructure

Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a through testing process for new versions of the backend before they are released to the public. You want the testing environment to scale in an economical way. How should you design the process?

• Create a scalable environment in GCP for simulating production load

• Use the existing infrastructure to test the GCP-based backend at scale

• Build stress tests into each component of your application using resources internal to GCP to simulate load

• Create a set of static environments in GCP to test different levels of load " for example, high, medium, and low

• Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine

• Google Cloud Storage, Google App Engine, Google Network Load Balancer

• Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer

• Google Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager

Mountkirk Games' gaming servers are not automatically scaling properly. Last month, they rolled out a new feature, which suddenly became very popular. A record number of users are trying to use the service, but many of them are getting 503 errors and very slow response times. What should they investigate first?

• Verify that the database is online

• Verify that the project quota hasn't been exceeded

• Verify that the new feature code did not introduce any performance bugs

• Verify that the load-testing team is not running their tool against production

Mountkirk Games needs to create a repeatable and configurable mechanism for deploying isolated application environments. Developers and testers can access each other's environments and resources, but they cannot access staging or production resources. The staging environment needs access to some services from production. What should you do to isolate development environments from staging and production?

• Create a project for development and test and another for staging and production

• Create a network for development and test and another for staging and production

• Create one subnetwork for development and another for staging and production

• Create one project for development, a second for staging and a third for production

Mountkirk Games wants to set up a real-time analytics platform for their new game. The new platform must meet their technical requirements. Which combination of Google technologies will meet all of their requirements?

• Kubernetes Engine, Cloud Pub/Sub, and Cloud SQL

• Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery

• Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow

• Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow

• Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to migrate from their current analytics and statistics reporting model to one that meets their technical requirements on Google Cloud Platform. Which two steps should be part of their migration plan? (Choose two.)

• Evaluate the impact of migrating their current batch ETL code to Cloud Dataflow.

• Write a schema migration plan to denormalize data for better performance in BigQuery.

• Draw an architecture diagram that shows how to move from a single MySQL database to a MySQL cluster.

• Load 10 TB of analytics data from a previous game into a Cloud SQL instance, and run test queries against the full dataset to confirm that they complete successfully.

• Integrate Cloud Armor to defend against possible SQL injection attacks in analytics files uploaded to Cloud Storage.

For this question, refer to the Mountkirk Games case study. You need to analyze and define the technical architecture for the compute workloads for your company, Mountkirk Games. Considering the Mountkirk Games business and technical requirements, what should you do?

• Create network load balancers. Use preemptible Compute Engine instances.

• Create network load balancers. Use non-preemptible Compute Engine instances.

• Create a global load balancer with managed instance groups and autoscaling policies. Use preemptible Compute Engine instances.

• Create a global load balancer with managed instance groups and autoscaling policies. Use non-preemptible Compute Engine instances.

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available. Which two steps should they take? (Choose two.)

• Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.

• Begin packaging their game backend artifacts in container images and running them on Google Kubernetes Engine to improve the ability to scale up or down based on game activity.

• Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.

• Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.

• Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants you to design a way to test the analytics platform's resilience to changes in mobile network latency. What should you do?

• Deploy failure injection software to the game analytics platform that can inject additional latency to mobile client analytics traffic.

• Build a test client that can be run from a mobile phone emulator on a Compute Engine virtual machine, and run multiple copies in Google Cloud Platform regions all over the world to generate realistic traffic.

• Add the ability to introduce a random amount of delay before beginning to process analytics files uploaded from mobile devices.

• Create an opt-in beta of the game that runs on players' mobile devices and collects response times from analytics endpoints running in Google Cloud Platform regions all over the world.