Amazon ML System Design interview questions probe your ability to design production-grade machine learning systems that operate at Amazon scale. These rounds are distinctive because they combine classic system-design rigor with ML-specific concerns: data pipelines and quality, feature stores, model training and serving trade-offs, monitoring and drift detection, latency and cost constraints, and experiment/rollout strategies. Interviewers evaluate how you clarify requirements, make principled trade-offs, estimate scale and cost, reason about reliability and observability, and connect ML choices back to business impact and operational ownership. Expect an open-ended, whiteboard-style conversation where you first ask clarifying questions, sketch a high-level architecture, and iterate into data, model lifecycle, serving, and monitoring. Good interview preparation includes practicing end-to-end designs, running back-of-the-envelope traffic and storage estimates, rehearsing trade-off explanations, and preparing examples of past production ML work framed to Amazon’s leadership principles. Emphasize testability, failure modes, and measurable success metrics; be ready to discuss deployment cadence, A/B tests, rollback plans, and cost optimization. With focused practice you can present a clear, pragmatic design that balances scalability, accuracy, and operational simplicity.