Google Cloud Professional Cloud Architect — Question 232

Your employer is a financial services company that 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 a single, large PostgreSQL database. Your development team struggles with slow deployment cycles, and the monolithic design of the startup's core application makes it difficult to integrate new. ML-powered fraud detection models. You need a long-term strategy that improves developer agility and positions the company to leverage Google Cloud's advanced data and AI capabilities for future innovations. What should you do?

Answer options

• 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.

Correct answer: C

Explanation

The correct answer is C because proposing a phased migration to a microservices architecture allows for improved agility and scalability, making it easier to integrate new ML models. Options A and B focus on specific solutions that do not address the overall architectural challenges, while D, although beneficial for data analytics, does not enhance the application's deployment process or flexibility.