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

70 practiced

For a pipeline composed of ingestion -> preprocessing -> model inference -> postprocessing, provide concrete examples of unit tests, integration tests, and end-to-end tests. For each testing level include three test cases with inputs and expected outcomes, and discuss trade-offs in speed, flakiness, and cost when running them in CI.

MediumSystem Design

101 practiced

Design a data ingestion validation strategy for a user-events pipeline that receives roughly 1M events per day. Provide a concise JSON schema or field table for event fields, and list test cases to validate schema enforcement, missing or malformed events, sampling-based anomaly detection, and backfill checks. Explain how to balance full validation vs sampling for cost.

EasyTechnical

69 practiced

Explain property-based testing and how it contrasts with example-based unit tests. Give a concrete Hypothesis-style example with two properties you would assert for a tokenization function: for instance, a round-trip property (join(tokens) normalized equals normalized input) and monotonic length property (tokenized length non-decreasing with added whitespace). Describe the generator strategies for inputs including Unicode and control characters.

HardSystem Design

74 practiced

Design a stress-testing plan to find concurrency bugs and memory leaks in an inference cluster serving very high throughput (for example 100k requests per second). Define the load generation strategy, distributed harness, resource and heap monitoring, chaos testing steps, and how to detect rare failures that only occur after sustained load or with specific request patterns.

HardTechnical

78 practiced

Write a test harness in Python pseudocode that simulates a worker failure during distributed training (for example with PyTorch Distributed Data Parallel). The harness should run a short deterministic training loop, inject a process kill or network partition, verify that a checkpoint is saved, resume training from the checkpoint, and assert that validation loss after resume is close to the non-failure baseline. Describe unit tests for corrupted checkpoint files and partial writes.

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