Edge Case Identification and Testing Questions

Focuses on systematically finding, reasoning about, and testing edge and corner cases to ensure the correctness and robustness of algorithms and code. Candidates should demonstrate how they clarify ambiguous requirements, enumerate problematic inputs such as empty or null values, single element and duplicate scenarios, negative and out of range values, off by one and boundary conditions, integer overflow and underflow, and very large inputs and scaling limits. Emphasize test driven thinking by mentally testing examples while coding, writing two to three concrete test cases before or after implementation, and creating unit and integration tests that exercise boundary conditions. Cover advanced test approaches when relevant such as property based testing and fuzz testing, techniques for reproducing and debugging edge case failures, and how optimizations or algorithmic changes preserve correctness. Interviewers look for a structured method to enumerate cases, prioritize based on likelihood and severity, and clearly communicate assumptions and test coverage.

MediumTechnical

71 practiced

Given a labelled dataset with timestamps, describe the concrete tests and checks you would run to detect label leakage before training. Include automated checks (for example, verifying no feature has greater predictive power on future labels than past labels), exploratory checks (feature-time correlations), and sample SQL or pandas checks to identify features with timestamps after the label timestamp. Describe what to do if you find potential leakage.

HardSystem Design

81 practiced

Design a comprehensive end-to-end testing and validation strategy for a nightly retraining pipeline that consumes streaming data with late-arriving events and backfills, must support schema evolution, and meet a production SLA of completing retraining within 4 hours. Describe unit tests, integration tests, synthetic test harnesses for late events, contract tests for schema evolution, and production validation gates (metrics, shadow testing, canary). Include how you would simulate late arrivals and backfills in tests.

MediumTechnical

95 practiced

You receive a dataset with categorical features including 'user_id' (hundreds of millions of uniques) and 'country' (tens of uniques). Design a testing strategy to catch edge cases caused by extremely high-cardinality categorical features: memory blowups from naive one-hot encoding, hashing collisions, unseen values in production, rare level handling, and target leakage in target encoding. Include unit tests, integration tests, and ideas to simulate production scale within tests.

EasyTechnical

97 practiced

Before implementing a rolling moving_average(series, window) function that handles missing timestamps and irregular spacing, write 2-3 concrete test cases (input timestamps and values, window size, expected output). Include tests for a single-element series, a window larger than the series length, and a series containing NaN values that should be ignored in averages. Show the input and expected numeric outputs for each case.

MediumTechnical

87 practiced

How should a data pipeline handle missing target values for supervised learning? Discuss strategies (exclude rows, impute, create 'unknown' class, semi-supervised approaches) and how you would write tests to ensure the training pipeline either excludes or correctly handles rows with missing labels. Include examples for classification and regression, and specify how to test that evaluation metrics gracefully handle missing targets.

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