This thing had like twelve sub-problems stapled together.
Start by clarifying requirements and scale (e.g., number of publishers, articles per day, users, latency targets) and then walk through the high-level architecture from ingestion to delivery. Focus on key components: ingestion pipeline, storage layers, personalized ranking, search, multi-region deployment, and compliance, while discussing trade-offs and how you would validate with A/B tests.
Pro tip: Emphasize how you would measure and iterate on the personalized ranking using A/B testing and offline metrics, and discuss how compliance (e.g., GDPR, CCPA) influences data storage and processing decisions.
Ask questions to understand the scale (publishers, articles/day, users), latency requirements, personalization depth, and compliance needs. Define functional and non-functional requirements.
Propose a scalable ingestion pipeline (e.g., Kafka, pub/sub) to handle thousands of publishers, with deduplication, normalization, and storage in a data lake and a serving database. Consider hot vs. cold storage.
Describe how to build personalized feeds using a ranking service that combines user features, article features, and real-time signals. Include a search layer (e.g., Elasticsearch) for keyword queries.
Explain how to achieve low-latency delivery via CDN and multi-region deployments, with data replication and consistency trade-offs. Discuss failover and disaster recovery.
Address compliance (GDPR, CCPA) by design: data anonymization, consent management, and data residency. Describe A/B testing framework to validate ranking changes and measure engagement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.