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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
94 practiced
Briefly explain property-based testing and give two concrete properties you'd test for a normalization function normalize(x) used before model input, and two properties for a model predict(x) API that should hold under simple input transformations (e.g., idempotence of normalization, invariance to zero-padding).
MediumTechnical
102 practiced
Implement a numerically stable softmax function in Python that handles very large and very small logits, and write two unit tests: one for extremely large positive values and one for extremely negative values. Explain briefly why your implementation avoids overflow/underflow.
EasyTechnical
92 practiced
Explain how integer overflow and underflow can appear in AI systems. Provide three specific examples (e.g., 8-bit quantized accumulators, timestamp arithmetic, bucketed histogram counters) and propose concrete unit or integration tests that would detect these issues before deployment.
HardTechnical
75 practiced
A generative image model can be asked to produce extremely large-resolution images or to iteratively refine an image many times, leading to unbounded memory growth. Design tests that bound resource usage, cause graceful failure on resource limits, and assert that partial outputs or error codes are returned rather than silent crashes. Include automated resource-limited CI tests.
HardSystem Design
79 practiced
Propose a test plan and harness to validate quantized models across different hardware targets: server CPU int8, mobile DSP, and mobile GPU. Include accuracy tolerance tests, stress tests for saturation/overflow, quantization-aware training checks, and automation to compare outputs to a float32 baseline.

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