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

0 practiced

Training loss becomes NaN after several epochs in a previously stable job. Describe a reproducible debugging plan and tests to isolate whether root cause is bad training data, exploding gradients, optimizer bug, or hardware nondeterminism. Include quick isolating tests, data checks, and regression tests to prevent recurrence.

MediumTechnical

0 practiced

An image preprocessing pipeline occasionally causes GPU OOM when batch size is 32 but works with 16. Describe a testing approach to reproduce and diagnose OOMs across multiple hardware configurations. Include unit/integration tests, synthetic workload generators, CI gating, and telemetry you would collect to triage memory growth over time.

MediumSystem Design

0 practiced

Design a test and monitoring strategy to detect data drift and distribution shift in production for a recommender system. Include statistical tests (KS, population stability index), thresholds, synthetic edge cases, and unit/integration tests that validate the monitoring pipeline itself (e.g., schema changes, missing stats).

EasyTechnical

0 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

0 practiced

Design a fuzz-testing approach for an NLP tokenizer and input pipeline: describe how to generate test inputs (random bytes, UTF-8 edge cases, malicious prompt injections), how to detect failures (exceptions, tokenization length explosion, encoding errors), and how to integrate fuzzers into CI so they find regressions early without noisy false positives.

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