This one sprawls fast if you don't pin down scope early.
Start by clarifying requirements and scope, then propose a high-level architecture that covers data ingestion, personalization, and serving. Dive into key components like ranking algorithms, real-time updates, and scalability, while discussing trade-offs and Amazon-specific considerations.
Pro tip: Emphasize how you would measure success and iterate using A/B testing and metrics like click-through rate and user engagement, showing a customer-obsessed mindset.
Ask questions to understand scale, latency, personalization signals, and content types. Define functional and non-functional requirements.
Outline the main components: data collection, user profiling, content processing, ranking, and serving. Sketch a block diagram.
Discuss the ranking algorithm (e.g., collaborative filtering, content-based, or hybrid), real-time vs batch processing, and storage choices.
Explain how to handle large-scale data, low-latency serving, fault tolerance, and cost optimization using AWS services.
Discuss trade-offs between personalization and diversity, freshness vs relevance, and how to measure and iterate using A/B testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.