Google Cloud Platform currently uses three case studies for a certain percentage of its PCA (Professional Cloud Architect) exam questions that serve as an additional context. These case studies describe a fictitious business and solution concept. This article presents possible solution implementations for these case studies.

Note:_ This article presents solutions for the current version (as of June 2020) of the case studies. These case studies might change in the future which might render these solution implementations invalid. Each case study is linked to the June 2020 version of the fictitious business and solution concept._

Case Study 1 — Mountkirk Games

Professional Cloud Architect Certification Case Study - Mountkirk Games

The description of the case study can be taken from the link above. The following illustration represents a high-level overview of what a possible solution infrastructure might look like.

This diagram represents a high-level view and doesn’t show all components necessary

Business requirements

• **Increase to a global footprint **— Use products such as MIGs in multiple regions around the world with a global load balancer. Store data in Spanner for a global reach. BigTable can also be used for automatic global replication (in total four regions around the world) and using app profiles one can designate which cluster to use in which case.
• **Improve uptime — downtime is loss of players **— Keep the system highly available and reliable. Use custom metrics in Stackdriver to be able to scale the system up and down. It is helpful to have Incident Response Teams with clear functional demarcations about everybody’s duties in case of an incident.
• Increase efficiency of the cloud resources we use — Monitor the average and peak usage of your cloud resources. It is recommended to export the metrics** data to BigQuery**, in order to analyse the cpu usage and other metrics for a period longer than 6 weeks (Cloud Monitoring keeps the metrics data for that long). Based on this data, decisions can be made on instance type for compute engine etc.
• Reduce latency to all customers — Use **Premium Network Tier. **Depending upon how congested traffic on the public internet is, this option can reduce latency considerably. Use Cloud CDN for your data. The latency between Europe and Asia is quite high compared to other regions so if possible, make sure there is a dedicated deployment in Asia-Pacific.

Technical requirements

1. Dynamically scale up or down based on game activity — Use MIGs
2. Connect to a transactional database service to manage user profiles and game state — Use Spanner
3. Store game activity in a timeseries database service for future analysis — Use BigTable
4. As the system scales, ensure that data is not lost due to processing backlogs — Use Pub/Sub
5. Run hardened Linux distro — Use Custom Image in Instance Template
6. Dynamically scale up or down based on game activity — Use Dataflow
7. Process incoming data on the fly directly from the game servers — Use Pub/Sub and Dataflow
8. Process data that arrives late because of slow mobile networks — Use Dataflow
9. Allow queries to access at least 10 TB of historical data — Use BigQuery
10. Process files that are regularly uploaded by users’ mobile devices — Use Dataflow

#gcp-certification #google-cloud-platform #case-study #certification-exam #cloud-architecture #cloud