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

0 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

1 practiced

List typical off-by-one errors in time-windowed computations (for example inclusive vs exclusive endpoints, window definitions for rolling sums, and index-based slicing mismatches). Provide a concise unit test example (inputs and expected output) demonstrating an inclusive vs exclusive misunderstanding and how your test would fail for an incorrect implementation.

HardTechnical

0 practiced

Design a property-based testing strategy for a feature pipeline where two independent transformations A and B are expected to commute (order should not matter). Specify properties to assert commutativity and produce counterexamples (e.g., clipping then normalization vs normalization then clipping). Describe how you would implement shrinking and minimal counterexample reporting to help debug failing properties.

MediumTechnical

0 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.

EasyTechnical

1 practiced

You have a Python function signature:

python

def safe_mean(numbers: List[float]) -> Optional[float]:
    """Return the mean of numeric values. Ignore NaNs. Return None if no finite values."""

Write three pytest unit tests that exercise realistic edge cases: empty list, list containing NaN and infinite values, very large values that could cause overflow if cast incorrectly, and a single-element list. Provide the pytest test code snippets (inputs and expected asserts).

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