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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Your organization has multiple teams building similar user behavior features, causing duplication and naming collisions. Propose a catalog/discovery and governance strategy to increase feature reuse: include metadata fields, search UX, approval/workflow, and incentives for contributors.

EasyTechnical

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List and briefly compare five common strategies to handle missing values in features (e.g., mean imputation, median, forward-fill, model-based imputation, indicator variables). For each strategy, describe one situation where it is appropriate and one where it may introduce bias.

MediumTechnical

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SQL: From an events table (user_id, event_type, occurred_at), write a query that computes a 'days_since_last_purchase' feature per user per day but avoid label leakage when training a model that predicts a purchase in the next 7 days. Describe your assumptions and how you ensure the feature won't peek into the future.

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.

EasyTechnical

0 practiced

Explain the difference between 'feature engineering' and a 'feature store'. For each, describe primary responsibilities, typical outputs, who owns them in an organization, and give two concrete examples: one example of a feature engineering transformation (e.g., sessionization) and one capability provided by a feature store (e.g., online low-latency serving).

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