A logistics firm routes time-critical operational decisions through a proprietary foundation model consumed entirely through one vendor's hosted API, with no rights to the weights and no fallback provider. The governance team is documenting the deployment risks that are distinctive to this proprietary, single-vendor arrangement rather than to AI deployment in general. Which two risks are most distinctive to this arrangement? Select TWO.
- AThe vendor can silently update the model behind the same endpoint, shifting behaviour in production and invalidating the firm's earlier validation results. Correct
- BConcentration on one provider with no portable weights leaves the firm exposed to outages, price moves, or discontinuation it cannot mitigate by switching. Correct
- CThe model could produce a biased or inaccurate output that harms an affected individual if the system is not adequately tested before launch.
- DStaff might over-rely on the system's recommendations and approve them without the scrutiny the oversight design assumed they would apply.
- EThe firm must keep training data accurate and representative so the model does not degrade as input patterns shift over its operational life.
Why A is correct: Correct: opaque vendor-side updates to a hosted proprietary model are a distinctive risk because the firm cannot pin a version it does not control.
Why B is correct: Correct: vendor lock-in and concentration risk are distinctive here because the closed weights and lack of a fallback remove any practical exit.
Why C is wrong: Tempting and real, but output bias is a general risk of any AI deployment, not one distinctive to a proprietary single-vendor hosted arrangement.
Why D is wrong: Tempting as a deployment concern, but automation bias arises in any human-oversight setup regardless of whether the model is proprietary or vendor-hosted.
Why E is wrong: Tempting because drift is a live concern, but a firm consuming a closed hosted model does not control its training data, and drift is a general deployment risk.