Design a Real-Time Feature Store
Company: Cognitiv
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Technical Screen
Quick Answer: This question evaluates a candidate's ability to design real-time feature store architectures, encompassing competencies in low-latency serving, point-in-time correctness for offline training, streaming ingestion, consistency between online and offline features, handling backfills and late events, and scalable monitoring of data quality and reliability. It is commonly asked to assess systems-design and data-engineering judgment for production-grade ML infrastructure, falls under the ML system design and data engineering domain, and tests both conceptual understanding of trade-offs (consistency, latency) and practical application skills for APIs, storage, and operational design.