Start by clarifying requirements and scale, then propose a high-level architecture that separates data ingestion, processing, and serving layers. Focus on how to handle 300M users with batch and real-time needs, and discuss trade-offs in data storage, computation, and consistency.
Pro tip: Emphasize data freshness and accuracy: credit scores are highly sensitive, so discuss how you'd handle data discrepancies, versioning, and audit trails. Also, mention cost optimization since processing 300M users can be expensive.
Ask about data sources, update frequency, latency requirements, and compliance needs (e.g., FCRA). Understand if scores are computed on-demand or precomputed.
Outline components: data ingestion from credit bureaus, a processing pipeline (batch/stream), a scoring engine, and a serving layer with APIs. Consider using a message queue for ingestion and a distributed processing framework like Spark.
Design schemas for raw credit data, user profiles, and computed scores. Choose storage: data lake for raw data, NoSQL for user profiles, and a fast KV store for serving scores. Discuss partitioning and indexing for 300M users.
Explain how to compute scores efficiently: use batch processing for periodic updates and stream processing for real-time changes. Discuss horizontal scaling, caching, and load balancing to handle high read throughput.
Discuss trade-offs: consistency vs. availability, cost vs. performance, and batch vs. real-time. Address fault tolerance, data versioning, and monitoring to ensure accuracy and compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.