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.

EasyTechnical

0 practiced

When a model inference endpoint receives null, NaN, or Inf values in features, what are reasonable fail-safe behaviors for classification vs regression endpoints? Propose three unit tests and one integration test to check these behaviors, including how to simulate malformed JSON or binary payloads that produce NaNs.

MediumTechnical

0 practiced

A deployed model shows a 12% accuracy drop for inputs shorter than 5 tokens. Describe how you would systematically enumerate similar problematic input subspaces, prioritize them, and add automated tests to fail builds when regressions occur for these subspaces. Include data collection, synthetic test generation, and CI gating strategies.

MediumTechnical

0 practiced

Write a pytest-style test plan to verify reproducibility of a simple model training run across two invocations. Include steps to capture and reset random seeds, fixture setup for deterministic data shuffling, and how to assert equivalence of model weights or outputs up to numerical tolerance. Mention GPU/CuDNN pitfalls and how to mitigate them in tests.

MediumTechnical

0 practiced

Explain the dataset-bisect technique to find a small subset of training data responsible for a regression or training instability. Provide a practical test plan that incorporates automated bisection into debugging and a regression test that prevents the same bad examples from being reintroduced.

HardTechnical

0 practiced

You need to prove monotonicity for a ranking model: if every feature in A >= corresponding feature in B, then score(A) >= score(B). Explain how you would construct a property-based test to check monotonicity, how to generate valid input pairs, how to handle ties and floating point noise, and how to limit the test search space for tractability.

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