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.

HardTechnical

75 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.

MediumSystem Design

73 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.

MediumSystem Design

71 practiced

Design a CI/CD pipeline for ML models that runs unit tests, data schema checks, model training smoke tests, evaluation/regression tests, and a canary or shadow deployment stage. Describe gating logic for promotions, artifact storage strategy, test frequency and cost controls, and how you would roll back on detected regressions.

MediumTechnical

71 practiced

You have a multi-class classification problem with a long-tail label distribution and rare classes representing 0.01% of examples. Propose test strategies and synthetic-data generation techniques that ensure the pipeline can detect performance regressions for rare classes (per-class precision/recall thresholds, minimum sample sizes, and confusion matrix slice checks). Explain how you would validate sampling and avoid false positives.

HardTechnical

85 practiced

Design a fuzzing campaign for a model-serving REST API that accepts multipart inputs (JSON metadata + binary files). Specify which fuzzers or libraries to use for structured parts and binary parts, how to generate grammar-based mutations for JSON, what invariants to assert (no crash, no unhandled exceptions, bounded memory), and a triage plan to turn crashes into reproducible minimal testcases.

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