Architect a geo-distributed Recommendation Engine
Last updated: February 2, 2026
Quick Overview
Design a geo-distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Adobe
February 2, 202622
6
3,216 solved
Design a geo-distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Adobe goes beyond model selection. This Technical Screen question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
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
- How would you handle a 10x increase in prediction requests?
- What is your model retraining strategy?
- How would you handle the cold start problem?
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