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.

EasyTechnical

0 practiced

Describe unit tests for model serialization/deserialization (saving and loading checkpoints) ensuring parameter equivalence, optimizer state restoration when required, and deterministic outputs for a fixed seed. Specify how to check floating-point tolerances and cross-framework compatibility (e.g., PyTorch -> ONNX) in tests.

EasyTechnical

0 practiced

Design unit and integration tests to ensure a text-generation model respects maximum output length limits, start/end special tokens (BOS/EOS), and truncation policies. Include tests for edge cases: zero-length prompts, extremely long prompts that trigger truncation, prompts saturated with special tokens, and streaming generation where chunking occurs.

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.

HardTechnical

0 practiced

Generative model outputs do not have a single correct answer. Design test oracles and automation strategies to validate quality, safety, and semantic correctness at scale. Include automated proxies (BLEU/ROUGE/BERTScore), classifiers for toxic or unsafe outputs, human-in-the-loop sampling protocols, and techniques to detect regressions over time.

HardTechnical

0 practiced

Discuss trade-offs and build a testing plan for deterministic vs nondeterministic testing in reinforcement learning systems where environment stochasticity is inherent. How would you validate policy correctness, avoid brittle tests, and ensure meaningful regression detection while coping with high variance?

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