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.

MediumTechnical

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Write a PostgreSQL query that computes a per-user z-score for daily purchase amount using a 90-day window. Given table `transactions(user_id, amount, occurred_at)`, output (user_id, occurred_date, amount_zscore_90d). Explain how you handle users with fewer than 2 days of history.

HardTechnical

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Design privacy-preserving feature computation strategies that allow using user-level data while complying with regulations (e.g., differential privacy, k-anonymity, masking). Explain trade-offs in utility vs privacy and how to measure the impact on model performance.

MediumTechnical

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How would you detect feature drift and concept drift in production? Describe a monitoring plan including metrics to track (e.g., distribution divergence, feature missingness), alert thresholds, and automated or manual remediation steps you would take when drift is detected.

HardTechnical

0 practiced

Design a metric to quantify the ROI of a feature store platform for your organization. Which inputs would you collect (engineering hours saved, feature reuse rates, reduction in model drift incidents) and how would you compute a single dashboard KPI that executives can use?

HardTechnical

0 practiced

You are given a feature that is high-cost to materialize but critical for small percentage of queries. Propose a tiered storage and serving approach (hot/warm/cold) including eviction policy, caching, and fallbacks, and estimate cost/latency trade-offs with an example calculation.

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