Edge Cases and Complex Testing Questions

Covers identification and systematic handling of edge cases and strategies for testing difficult or non deterministic scenarios. Topics include enumerating boundary conditions and pathological inputs, designing test cases for empty, single element, maximum and invalid inputs, and thinking through examples mentally before and after implementation. Also covers complex testing scenarios such as asynchronous operations, timing and race conditions, animations and UI transients, network dependent features, payment and real time flows, third party integrations, distributed systems, and approaches for mocking or simulating hard to reproduce dependencies. Emphasis is on pragmatic test design, testability trade offs, and strategies for validating correctness under challenging conditions.

HardTechnical

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Design tests for real-time multimodal pipelines that synchronize audio, video, and text streams. Include synthetic synchronized input generation, jitter and packet loss simulation, checks for misalignment, missing frames, and latency-induced inconsistencies, and strategies for automated regression capture for failing alignment cases.

MediumTechnical

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Design tests to verify correctness and consistency of parameter synchronization in distributed data-parallel training. Include test cases for dropped gradients, straggler nodes, checkpoint consistency across ranks, and deterministic replay of failed iterations to ensure no silent corruption of model state.

MediumTechnical

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Design robust tests for a streaming data pipeline feeding an online learning model. Include cases for delayed events, out-of-order arrivals, duplicates, partial failures, and schema evolution. Describe tools and synthetic data generation that allow deterministic replay of these scenarios for debugging.

HardTechnical

0 practiced

Design end-to-end tests to validate differential privacy guarantees (for example DP-SGD) in a training pipeline: include unit tests for noise addition layers, verification of privacy accountant outputs across multiple training runs, and end-to-end checks that utility remains within acceptable bounds while privacy budgets are respected during hyperparameter sweeps.

MediumTechnical

0 practiced

Design testing strategies to detect adversarial examples for vision and NLP models. Include methods for generating adversarial inputs (FGSM, PGD, paraphrase or synonym substitution), defenses to validate (adversarial training, input transformations), and quantitative metrics for measuring robustness.

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