Covers the systematic identification, analysis, and mitigation of edge cases and failures across code and user flows. Topics include methodically enumerating boundary conditions and unusual inputs such as empty inputs, single elements, large inputs, duplicates, negative numbers, integer overflow, circular structures, and null values; writing defensive code with input validation, null checks, and guard clauses; designing and handling error states including network timeouts, permission denials, and form validation failures; creating clear actionable error messages and informative empty states for users; methodical debugging techniques to trace logic errors, reproduce failing cases, and fix root causes; and testing strategies to validate robustness before submission. Also includes communicating edge case reasoning to interviewers and demonstrating a structured troubleshooting process.
MediumTechnical
37 practiced
In Python using PyTorch or TensorFlow, implement (pseudocode or real code) an inference wrapper that attempts a forward pass on GPU, catches GPU out-of-memory errors, frees GPU resources safely, and retries inference on CPU while emitting metrics/logs that indicate the fallback. Discuss race conditions, how to avoid partial-state corruption, and how to maintain per-request atomicity when falling back.
MediumSystem Design
49 practiced
Design a pragmatic test plan for a model-serving system that must robustly handle edge cases: malformed inputs, network timeouts, permission denials when fetching features or model artifacts, corrupted model files, and GPU OOM. List types of tests (unit, integration, e2e, chaos/fault-injection), example test cases for each edge case, automation approach, monitoring to validate readiness, and rollback criteria. The system should meet p95 latency <200ms and run within a 4GB memory limit under normal load.
MediumTechnical
38 practiced
Write pytest-style unit tests to validate model serialization/deserialization across minor version changes. Tests should verify: model loads without exceptions, predictions before and after save/load are within numeric tolerance when using a fixed seed, and that corrupted or partial files raise clear exceptions. Describe how you would simulate a corrupted checkpoint and assert graceful failure.
HardTechnical
47 practiced
A model-serving job intermittently fails with 'permission denied' when loading artifacts from cloud storage. Provide a step-by-step debugging and mitigation plan that covers IAM role checks, temporary credentials/token refresh, signed URL expiration, eventual consistency in ACLs, local caching strategies, and tests you would run to validate the fix across dev/staging/prod environments.
HardSystem Design
33 practiced
Design an automated canary and rollback system for model deployments across two regions that safely tests new model versions with live traffic. Requirements: handle up to ~1M users/day, automatically rollback when a primary metric degrades beyond a 1% absolute effect (with statistical testing), support user bucketing and progressive ramp-up, and isolate region-specific failures. Describe traffic routing, metric collection, sample-size calculation, detection windows, and safety measures to avoid correlated failures.
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