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

Explain how duplicate or near-duplicate training examples can bias models. Provide five concrete tests you would run on a training dataset to detect exact duplicates, near-duplicates, repeated user sessions, repeated label copies, and near-duplicate text or image entries. For each test describe expected detection method (hashing, similarity thresholds) and a remediation strategy.

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.

HardTechnical

0 practiced

You will deploy a model update that uses transfer learning (warm-start) from a previous checkpoint. Design a test plan to ensure no regressions on critical slices after warm-starting, verify new classes are handled correctly, and that checkpoint compatibility is preserved. Include offline regression tests, shadow-mode validation, and rollback criteria.

EasyTechnical

0 practiced

You implement padding and truncation logic for sequences with max_seq_len=128 for an NLP model. Write test cases that validate correct behavior for input lengths 0, 127, 128, and 129, including attention masks and special token placement. Describe off-by-one risks and how your tests catch them.

HardTechnical

0 practiced

Implement a simple adversarial test harness (pseudocode or Python) that generates FGSM or PGD adversarial examples against a small image classifier and asserts that the model's top-1 accuracy degradation is within an acceptable bound or that a defense reduces the attack success rate. Include considerations for reproducibility, choice of epsilon, and how to integrate this as a periodic robustness test.

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