Start by clarifying requirements and scale, then design a high-level architecture with ingestion, routing, detection, and alerting components. Focus on how routing rules are dynamically applied based on trader/exchange identity, and discuss trade-offs in latency, scalability, and accuracy.
Pro tip: Emphasize the need for a rule engine that can be updated in real-time without redeploying, and discuss how to handle false positives to maintain customer trust.
Ask about scale (orders per second), latency requirements, types of manipulation to detect, and customer expectations for alerts.
Outline components: ingestion layer (e.g., Kafka), routing service, detection algorithms, alerting service, and customer-facing API. Sketch data flow.
Design a rule-based router that uses trader/exchange metadata to direct orders to appropriate detection algorithms. Discuss dynamic rule updates.
Explain how detection algorithms process orders (e.g., streaming vs batch), and how suspected events are surfaced to customers with context.
Discuss trade-offs: latency vs accuracy, cost vs performance. Address scaling, fault tolerance, and data consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.