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.

MediumTechnical

0 practiced

Your team's test suite is flaky and cannot be run reliably in parallel. Describe a systematic approach to reduce flakiness and enable safe parallel execution. Include detection, isolation of shared resources, use of ephemeral resources, seeding, and CI configuration changes.

EasyTechnical

0 practiced

Using Jest, write a unit test for the following JavaScript function that uses setTimeout to delay execution. Show how to avoid waiting real time and assert the callback was called with the expected value.

javascript

function delayedHello(cb, ms) {
  setTimeout(() => cb('hello'), ms)
}

Include setup to use fake timers and an assertion on the callback invocation.

HardSystem Design

0 practiced

Design an automated performance and scalability testing strategy that fits into CI/CD for a service with 5 million daily users. Specify what tests to run on PR, on release, and nightly; how to provision load environments; how to detect regressions reliably; and how to avoid false positives caused by noisy environments.

HardTechnical

0 practiced

Your product handles personal data and integration tests need realistic datasets without exposing production PII. Design a solution for synthetic data generation, privacy-preserving transformations, and CI seeding strategies that balance fidelity and compliance requirements.

HardTechnical

0 practiced

For a service that uses a learned ranking model, design test and validation strategies: unit tests for preprocessing, offline evaluation suites, integration tests for inference endpoints, and production monitors for data drift, latency, and model degradation. Explain canary experiments and rollback criteria for model updates.

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