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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Design approaches for protecting sensitive information used in feature engineering. Compare role-based access control with attribute-based access control, data masking and tokenization, differential privacy for aggregate features, federated feature computation, and secure enclave approaches. Discuss trade-offs in model utility, auditability, and operational complexity.

EasyTechnical

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Explain what a feature store is and why organizations use feature stores in production ML systems. In your answer cover: core capabilities (offline and online storage, serving, metadata/catalog), how a feature store improves training-serving consistency, and typical components of a feature store architecture including ingestion, materialization, online serving, and metadata.

HardTechnical

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Describe a comprehensive validation framework for features that covers unit tests, statistical tests, schema validation, semantic checks, and integration tests. For each category explain important assertions such as null-rate thresholds, distribution comparisons, correlations with target, how tests run in CI, and actions on failures for blocking promotions.

EasyTechnical

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What is feature leakage (label leakage) and why is it dangerous? Provide a simple, concrete example where a feature is derived from future information, explain how it inflates training performance, and describe how it fails in production.

EasyTechnical

0 practiced

Implement a Python function preprocess(records) that accepts a list of dicts representing user events where each dict has keys 'user_id', 'age' (may be null), 'country', and 'purchase_amount'. Return a list of feature dicts with: 1) imputed 'age' using the median over non-null ages, 2) one-hot encoding for top-3 countries 'US','IN','CN' and a bucket 'other', 3) normalized 'purchase_amount' scaled min-max to [0,1] across input records. Specify input/output contracts.

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