Start by clarifying requirements (scale, latency, personalization, freshness) and then walk through the system from client to backend, covering API design, data storage, indexing, ranking, caching, scalability, and failure handling. Emphasize trade-offs and how you would measure success with A/B testing, especially in a fintech context like SoFi where compliance and personalization matter.
Pro tip: Tie your design to business metrics (e.g., conversion, engagement) and mention how you'd A/B test ranking changes without disrupting user experience. Also, highlight the importance of fallback mechanisms and graceful degradation to maintain trust in a financial app.
Ask questions to understand expected QPS, latency SLA, data sources (e.g., user queries, financial products), personalization needs, and compliance constraints. Define functional and non-functional requirements.
Define a simple REST or gRPC API for suggestions (e.g., GET /suggest?q=). Model data as a trie or inverted index with terms, frequencies, and metadata (e.g., product type, user segments).
Explain how to build and update indexes (e.g., using a trie for prefix matching, or Elasticsearch for scalability). Describe ranking signals: popularity, recency, personalization (user history), and business rules.
Discuss caching layers (CDN, Redis) for hot queries, sharding for horizontal scaling, and a pipeline for freshness (e.g., Kafka for real-time updates, batch jobs for reindexing).
Describe A/B testing framework for ranking changes, with metrics and guardrails. Cover failure modes: index downtime, cache misses, and fallback to static suggestions or degraded service.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.