Which task is drift monitoring specifically designed to accomplish?
Select an answer to reveal the explanation.
Short Explanation
That's the whole job description—notice when the world the model sees has changed from the world it was trained and validated on. You're not fixing storage, speed, or access; you're flagging meaningful divergence.
Full Explanation
Drift monitoring compares live data and model outputs against the validated baseline and flags meaningful divergence, which is its specific and narrow purpose. Reducing the storage footprint of historical training data is incorrect because storage optimisation is a data-engineering concern, unrelated to detecting behavioural change. Speeding up the model's inference response time is incorrect because inference speed is a performance-engineering concern, not something drift monitoring addresses. Enforcing role-based access to the model's API endpoint is incorrect because access control is a security function, separate from monitoring behaviour. For the exam, keep drift monitoring's purpose narrow: detecting divergence from the validated baseline, nothing else.