Testability and Testing Practices Questions

Emphasizes designing code for testability and applying disciplined testing practices to ensure correctness and reduce regressions. Topics include writing modular code with clear seams for injection and mocking, unit tests and integration tests, test driven development, use of test doubles and mocking frameworks, distinguishing meaningful test coverage from superficial metrics, test independence and isolation, organizing and naming tests, test data management, reducing flakiness and enabling reliable parallel execution, scaling test frameworks and reporting, and integrating tests into continuous integration pipelines. Interviewers will probe how candidates make code testable, design meaningful test cases for edge conditions, and automate testing in the delivery flow.

EasyBehavioral

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Behavioral: Tell me about a time you improved test coverage or test quality for an ML project. Structure your answer using STAR: what was the Situation, what Task were you addressing, which Actions did you take to change the testing practice, and what Results did you achieve in measurable terms?

EasyTechnical

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Write a short pytest fixture that creates an isolated temporary directory for a test that writes model artifacts and ensures cleanup. Name the fixture and show how a test would use it. Focus on clarity and test isolation rather than full implementation details.

HardTechnical

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Propose a test plan and test cases to validate model artifact serialization and backward compatibility across versions. Include tests for format changes, config schema evolution, and how to test that older clients can still load models or fail gracefully with migration messages.

HardTechnical

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Explain how you would test the stability and fidelity of model explanations produced by SHAP or LIME. Propose concrete automated tests that validate explanation consistency across similar inputs, sensitivity to feature perturbation, and plausibility relative to known feature importance for synthetic datasets.

HardTechnical

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A model takes 24 hours to train. You must provide fast CI feedback while ensuring safety before production. Design a CI strategy that includes smoke tests, shortened training runs, surrogate models, and gating rules so developers get quick feedback without running full training on every PR.

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