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.

HardTechnical

0 practiced

A production model's business metrics have degraded over the last month. Walk through a reproducible, prioritized debugging plan to identify the root cause. Consider data issues, feature pipeline changes, model code changes, label shift, infrastructure changes, and how you would verify each hypothesis with tests or instrumentation.

MediumTechnical

0 practiced

Implement a stratified train/test split function in Python that takes arrays X and y and a fraction test_size, and returns X_train, X_test, y_train, y_test preserving class proportions. Do not use scikit-learn. Explain how you ensure randomness while preserving class distribution and reproducibility.

MediumTechnical

0 practiced

You have a Spark job that is running slowly on a large dataset. Outline a systematic debugging and optimization approach: which Spark UI metrics and logs you would inspect, common causes like skew and shuffle, and specific optimizations such as caching, partition tuning, and broadcast joins.

EasyTechnical

0 practiced

What is a RESTful API? List the main HTTP methods used in REST and explain when to use each. Also describe the meaning and typical use-cases for these HTTP status codes in API design: 200, 201, 204, 400, 401, 403, 404, 409, 500.

MediumTechnical

0 practiced

Standard k-fold cross-validation is often inappropriate for time-series forecasting. Describe at least two cross-validation strategies suitable for time-series data, explain how they prevent leakage, and how you would choose the validation window sizes for seasonal series.

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