almost the same question in the official google practice exam:

A. Kubernetes Engine, Cloud Pub/Sub, and Cloud SQL
B. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
C. Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
D. Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc

Correct answer
B. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
Feedback
A is not correct because Cloud SQL is the only storage listed, is limited to 10 TB of storage, and is better suited for transactional workloads. Mountkirk Games needs queries to access at least 10 TB of historical data for analytic purposes.
B is correct because:
-Cloud Dataflow dynamically scales up or down, can process data in real time, and is ideal for processing data that arrives late using Beam windows and triggers.
-Cloud Storage can be the landing space for files that are regularly uploaded by users’ mobile devices.
-Cloud Pub/Sub can ingest the streaming data from the mobile users.
BigQuery can query more than 10 TB of historical data.

C is not correct because Cloud SQL is the only storage listed, is limited to 10TB of storage, and is better suited for transactional workloads. Mountkirk Games needs queries to access at least 10 TB of historical data for analytic purposes.

D is not correct because Mountkirk Games needs the ability to query historical data. While this might be possible using workarounds, such as BigQuery federated queries for Cloud Storage or Hive queries for Cloud Dataproc, these approaches are more complex. BigQuery is a simpler and more flexible product that fulfills those requirements.

Let's break down the requirements....
* Use only fully managed services. Excludes C (GKE) and D (GCE).
* Allow SQL queries to access at least 10 TB of historical data. Has to be SQL, and the dataset is too large for CloudSQL. So CloudSpanner or BigQuery
* Process files that are regularly uploaded by users’ mobile devices. This points to CloudStorage.
* Process data that arrives late because of slow mobile networks. Most likely Cloud PubSub
* Dynamically scale up or down based on game activity.
* Process incoming data on the fly directly from the game servers.

So I would say A

@Eikistein The question is about analytics platform and not backend platform. They need SQL queries on datasets atleast 10GB which is the least for Cloud SQL. That leaves with BigQuery.

D
Check Technical Requirements of Mountkirk
- Dynamically scale up or down based on game activity.
- Connect to a managed NoSQL database service.
- Run customized Linux distro.

And their Solution Concept
They plan to deploy the game’s backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics

NoSQL = that eliminates options B,C and E
D has Compute Engine (see solution concept) and dataproc (platform for data processing, analytics and machine learning)