Feature Engineering and Feature Stores Questions

Designing, building, and operating feature engineering pipelines and feature store platforms that enable large scale machine learning. Core skills include feature design and selection, offline and online feature computation, batch versus real time ingestion and serving, storage and serving architectures, client libraries and serving APIs, materialization strategies and caching, and ensuring consistent feature semantics and training to serving consistency. Candidates should understand feature freshness and staleness tradeoffs, feature versioning and lineage, dependency graphs for feature computation, cost aware and incremental computation strategies, and techniques to prevent label leakage and data leakage. At scale this also covers lifecycle management for thousands to millions of features, orchestration and scheduling, validation and quality gates for features, monitoring and observability of feature pipelines, and metadata governance, discoverability, and access control. For senior and staff levels, evaluate platform design across multiple teams including feature reuse and sharing, feature catalogs and discoverability, handling metric collision and naming collisions, data governance and auditability, service level objectives and guarantees for serving and materialization, client library and API design, feature promotion and versioning workflows, and compliance and privacy considerations.

HardTechnical

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Propose an automated system to detect label leakage at scale for thousands of features. Describe heuristics, statistical tests, and metadata signals you would use (e.g., leak-like correlation with future label, timestamp alignment anomalies), and how you'd surface suspected leakage to feature owners with confidence scores.

HardSystem Design

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You operate a feature pipeline that must handle out-of-order events and late arrivals while remaining cost-efficient. Describe an algorithmic and system design that ensures correctness for aggregations (e.g., daily counts), allows efficient backfills, and minimizes recomputation cost. Discuss watermarking, delta/upsert stores, and incremental recompute strategies.

HardSystem Design

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Define SLOs and guarantees for an online feature serving system that must serve 100k requests per second with p99 latency under 50ms for 95% of features, while some features require freshness under 1 minute. Propose an architecture and trade-offs to meet latency and freshness simultaneously, and discuss mechanisms for admission control under overload.

HardTechnical

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As a platform lead, propose policies and technical features that encourage cross-team feature reuse and prevent duplication. Include catalog features, governance processes, discoverability metrics, and organizational incentives (e.g., usage-based credit, SLAs) you would implement.

HardTechnical

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Design monitoring and observability for feature pipelines and the feature store: list the primary telemetry you would collect (e.g., freshness, completeness, drift, compute-job durations, error rates), how you'd correlate telemetry to feature lineage, and the alerting/response process when metrics indicate a pipeline failure or feature degradation.

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