Architect a real-time Feature Flag Engine
Last updated: December 28, 2025
Quick Overview
Design a real-time feature flag system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
MongoDB
December 28, 202577
4
4,729 solved
Design a real-time feature flag system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from MongoDB's System Design Round tests your ability to think about ML systems at scale. The interviewer expects discussion of data quality, feature stores, model serving infrastructure, and A/B testing strategy.
What the Interviewer Expects
- Map the business problem to a concrete ML objective
- Propose reasonable features and a baseline model
- Discuss basic model evaluation metrics
- Outline a simple serving architecture
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- What is your model retraining strategy?
- How would you handle a 10x increase in prediction requests?
- How would you run A/B tests on different model versions?
- How would you handle the cold start problem?
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