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Which of the following methods should a data scientist use just before switching to a potential replacement model?
Correct Answer: A
# A/B testing allows a controlled experiment comparing the performance of two models - the current (A) vs. the candidate (B) - on live data. It's an industry best practice to validate real-world behavior before full replacement. Why the other options are incorrect: * B: Performance monitoring helps detect drift but doesn't directly compare models. * C: CI/CD automates deployment but doesn't evaluate performance differences. * D: Containerization packages the model but doesn't test it comparatively. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 5.5:"A/B testing is a recommended approach to validate model performance before switching versions in production." * ML System Operations Guide, Chapter 6:"Use A/B testing to ensure new models outperform baselines before full rollout." -
