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.

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.

EasyTechnical

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You're integrating a third-party labeling API used for online fine-tuning. Describe how you would mock this external service in unit and integration tests to simulate successful responses, slow responses, transient failures (5xx), authorization errors (401), and incorrect data schemas. Include approaches for contract testing and validating request/response shapes.

MediumTechnical

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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.

MediumSystem Design

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Design a canary testing and rolling deployment strategy for a new model version using production shadowing and gradual traffic ramp-up. Specify which metrics to monitor (latency, accuracy, error-rate, business KPIs), threshold-based rollback rules, and how to automate gating for promotion to full traffic.

HardTechnical

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You're upgrading the tokenizer used by a production NLP service. Design a comprehensive test plan to ensure backward compatibility, detect tokenization-induced semantic shifts, preserve model performance, and handle differences in vocabulary and special tokens. Include migration strategies, dataset checks, and traffic-splitting tests.

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