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

You maintain a JSON-based tokenizer function used by an NLP service: def tokenize(json_input: str) -> List[str]. Describe three unit tests to add using pytest that exercise edge cases: empty string, very large string (e.g., 1M chars), and malformed JSON containing control characters. For each test include expected behavior (exception vs sanitized output) and how to assert correctness without relying on external corpora.

HardTechnical

0 practiced

Explain differential testing for ML systems: running a reference implementation versus an optimized or new implementation and automatically finding behavioral divergences. Describe test harness components, oracle selection (what counts as a bug), input generation strategies, and how to triage and prioritize discovered divergences.

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.

EasyTechnical

0 practiced

List common off-by-one and boundary errors that occur when handling padded sequences (for example, calculating effective length, attention masks, slicing windows). For each of three examples, describe a short unit test and an integration test demonstrating the bug and its fix.

HardSystem Design

0 practiced

Architect an automated ML testing framework for CI that covers unit tests, property-based tests, fuzz tests, integration tests, performance tests, fairness/regression checks, and canary deployments. Specify components (test runner, artifact storage, test data management), gating policies, parallelization strategies for expensive tests, and how to surface failures for fast triage.

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