Technical Foundation and Self Assessment Questions

Covers baseline technical knowledge and the candidate's ability to honestly assess and communicate their technical strengths and weaknesses. Topics include fundamental infrastructure and networking concepts, operating system and protocol basics, core development and platform concepts relevant to the role, and the candidate's candid self evaluation of their depth in specific technologies. Interviewers use this to calibrate how technical the candidate is expected to be, identify areas for growth, and ensure alignment of expectations between product and engineering for collaboration.

EasyTechnical

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What is a feature store? Describe the difference between offline (batch) features and online (real-time) features, and list three problems a feature store solves in production machine-learning pipelines.

HardTechnical

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How would you secure ML model endpoints against model inversion, model extraction attacks, and API abuse? List concrete controls at the API level, model-level defenses, and organizational monitoring you would implement while balancing usability for legitimate users.

MediumTechnical

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You have a slow SQL query that joins two large tables. Describe the steps you would take to analyze and optimize the query. Explain the use of EXPLAIN ANALYZE, indexing strategies, join order, partitioning, and when to consider denormalization or materialized views.

HardSystem Design

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Design an end-to-end serving architecture to support online inference at 1000 requests per second with P95 latency under 50 ms. Include components for feature retrieval, model serving, caching, autoscaling, A/B testing, monitoring, and how you would ensure feature freshness and low tail latency across regions.

EasyTechnical

0 practiced

Define overfitting and underfitting in predictive modeling. Describe two regularization techniques and one validation strategy you would use to detect and reduce overfitting on a tabular dataset used for regression.

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