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QuestionQ87

Designing and planning a cloud solution architecture

Your employer, a financial services company, recently acquired a popular fintech startup. The startup's core application is a monolithic Python application running on a managed instance group of Compute Engine virtual machines with one large PostgreSQL database. Your development team faces slow deployment cycles, and the monolithic architecture of the startup's core application makes integrating new ML-powered fraud-detection models difficult. You need a long-term strategy that increases developer agility and positions the company to use Google Cloud's advanced data and AI capabilities for future innovation. What should you do?

  • A Deploy the ML fraud detection model to a Vertex AI endpoint. Create a REST API for the model and modify the monolithic Python application to call this endpoint for real-time fraud analysis.
  • B Containerize the application, deploy it to Google Kubernetes Engine (GKE), and migrate the PostgreSQL database to Cloud SQL for PostgreSQL.
  • C Propose a phased, event-driven migration to a microservices architecture. Use Pub/Sub for asynchronous communication and deploy the fraud models on Vertex AI endpoints.
  • D Migrate the PostgreSQL database to Cloud SQL for PostgreSQL. Replicate the data into BigQuery using Datastream, and then train and deploy the fraud detection models directly within BigQuery using BigQuery ML.
Explanation

A phased migration to microservices lets teams deploy and evolve business capabilities independently instead of releasing a tightly coupled monolith. Pub/Sub provides asynchronous, decoupled event communication between services, allowing fraud-detection and future data consumers to be added without tightly integrating them into the original application. Vertex AI endpoints provide managed online model deployment and prediction serving.

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