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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.

MediumTechnical
0 practiced
Describe how to build reproducibility tests that detect unintended changes due to RNGs, GPU nondeterminism, or library upgrades. Propose a set of CI checks such as small seeded trainings, weight checksum comparisons, deterministic inference on canonical inputs, and environment artifact capture. Explain how to interpret small numeric differences versus significant regressions.
HardTechnical
0 practiced
Write a pytest + Hypothesis style test that generates random batches of embedding indices for an embedding lookup and asserts: (1) no index is out of bounds, (2) negative sampling produces indices not present in the positive set, and (3) collisions in hash-bucketed embeddings behave within expected rates. Provide test skeleton and discuss strategies to shrink failing cases to minimal examples.
HardTechnical
0 practiced
After a software dependency upgrade you observe small but consistent changes in model predictions. Propose a testing strategy to detect, attribute, and prevent regressions due to library or hardware upgrades. Include checks such as deterministic inference on canonical inputs, checksum comparisons of serialized weights, differential rollout tests, pinned environments or container hashes, and automated alerts for significant drift.
MediumSystem Design
0 practiced
Design an integration test suite for a gRPC model-serving endpoint that supports dynamic batching and must handle up to 2k req/s. Tests should validate input schema enforcement, correctness of batching (ordering and padding), behavior under backpressure, and graceful degradation when nodes are overloaded. Outline tools and a test harness architecture and include sample test cases.
EasyTechnical
0 practiced
Explain property-based testing and how it contrasts with example-based unit tests. Give a concrete Hypothesis-style example with two properties you would assert for a tokenization function: for instance, a round-trip property (join(tokens) normalized equals normalized input) and monotonic length property (tokenized length non-decreasing with added whitespace). Describe the generator strategies for inputs including Unicode and control characters.

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