Raising Standards and Quality Expectations Questions
Examples of raising quality standards in your team or organization, improving engineering practices, pushing for excellence even when harder path. How you prevent mediocrity.
EasyBehavioral
0 practiced
Tell me about a time when you identified low-quality practices in an AI project and successfully raised standards across your team. Using the STAR framework, describe the situation, the concrete actions you took (tests, code reviews, automation, documentation), the measurable results, and lessons learned. If you don't have a real example, outline a plausible and prioritized plan you would execute.
EasyTechnical
0 practiced
You have a simple text preprocessing function: preprocess_text(s) -> returns lowercased text with punctuation removed and collapsed whitespace, and '' for None input. Describe at least five pytest unit tests you would write to cover normal behaviour, None input, unicode, punctuation stripping, whitespace collapsing, and idempotence. For each test indicate what it guards against.
EasyTechnical
0 practiced
Explain the difference between model verification and model validation in the context of production AI systems. Provide concrete examples of checks or tests that belong to each phase (for example: unit tests, type checks, determinism checks vs. holdout evaluation, A/B testing, business-metric validation) and explain why both are required to raise quality standards across an AI engineering team.
HardTechnical
0 practiced
Design a comprehensive test strategy to evaluate adversarial robustness for an image classification model. Cover white-box and black-box attack evaluations, metrics to quantify robustness (e.g., accuracy under L2/Linf constraints), automated CI-friendly tests, thresholds that block deployment, and mitigation strategies such as adversarial training or input preprocessing. Address compute costs and false positives in CI.
MediumTechnical
0 practiced
You need to implement automated data validation that runs both at training time and as part of production inference checks. Describe a practical implementation using tools like Great Expectations or TFDV: how to define expectations, where to run them, how to handle alerts and false positives, and how to incorporate schema evolution policies.
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