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PROFESSIONAL-CLOUD-ARCHITECT · Question #317

Case Study: 1 - Mountkirk Games Case Study Company Overview Mountkirk Games makes online, session-based. multiplayer games for the most popular mobile platforms. Company Background Mountkirk Games…

The correct answer is C. Pub/Sub, Dataflow and BigQuery. Option C is correct because Pub/Sub, Dataflow, and BigQuery form Google Cloud's canonical managed streaming analytics pipeline: Pub/Sub ingests real-time telemetry from game servers, Dataflow processes the stream on-the-fly and handles late-arriving data via…

Submitted by hassan_iq· Mar 30, 2026Designing and planning a cloud solution architecture

Question

Case Study: 1 - Mountkirk Games Case Study Company Overview Mountkirk Games makes online, session-based. multiplayer games for the most popular mobile platforms. Company Background Mountkirk Games builds all of their games with some server-side integration and has historically used cloud providers to lease physical servers. A few of their games were more popular than expected, and they had problems scaling their application servers, MySQL databases, and analytics tools. Mountkirk's current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting. Solution Concept Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics and take advantage of its autoscaling server environment and integrate with a managed NoSQL database. Technical Requirements Requirements for Game Backend Platform 1. Dynamically scale up or down based on game activity. 2. Connect to a managed NoSQL database service. 3. Run customized Linx distro. Requirements for Game Analytics Platform 1. Dynamically scale up or down based on game activity. 2. Process incoming data on the fly directly from the game servers. 3. Process data that arrives late because of slow mobile networks. 4. Allow SQL queries to access at least 10 TB of historical data. 5. Process files that are regularly uploaded by users' mobile devices. 6. Use only fully managed services CEO Statement Our last successful game did not scale well with our previous cloud provider, resuming in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the gams to target users. CTO Statement Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers. CFO Statement We are not capturing enough user demographic data usage metrics, and other KPIs. As a result, we do not engage the right users. We are not confident that our marketing is targeting the right users, and we are not selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue. For this question, refer to the Mountkirk Games case study. Mountkirk Games is building a new multiplayer game that they expect to be very popular. They want to be able to improve every aspect of the game and the infrastructure. To do this, they plan to create a system for telemetry analysis. They want to minimize effort, maximize flexibility, and ease of maintenance. They also want to be able to perform real-time analyses. Which of the following services may help to fulfill these requirements?

Options

  • APub/Sub and Big Table
  • BKubeflow
  • CPub/Sub, Dataflow and BigQuery
  • DPub/Sub and Cloud Spanner

How the community answered

(26 responses)
  • A
    12% (3)
  • B
    4% (1)
  • C
    77% (20)
  • D
    8% (2)

Explanation

Option C is correct because Pub/Sub, Dataflow, and BigQuery form Google Cloud's canonical managed streaming analytics pipeline: Pub/Sub ingests real-time telemetry from game servers, Dataflow processes the stream on-the-fly and handles late-arriving data via windowing/watermarks, and BigQuery provides serverless SQL analytics over petabytes of historical data - all fully managed with no infrastructure to maintain.

Option A (Pub/Sub + Bigtable) fails because Bigtable is a NoSQL key-value store optimized for low-latency reads/writes; it does not support the SQL queries on 10 TB+ of historical data that the requirements demand.

Option B (Kubeflow) is entirely off-target - Kubeflow is an ML workflow orchestration platform on Kubernetes, not a telemetry or streaming analytics service.

Option D (Pub/Sub + Cloud Spanner) misses the mark because Spanner is an OLTP relational database built for transactional consistency, not large-scale analytical queries; it also lacks stream processing for late data.

Memory tip: Think of the pipeline as three verbs - publish, flow, query - mapping directly to Pub/Sub → Dataflow → BigQuery. If an exam question mentions real-time ingestion + late data handling + SQL on big historical data, that trio is almost always the answer.

Topics

#Data Analytics#Streaming Data#Data Warehousing#Managed Services

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