Specification testing, which involves the direct measurement of the circuit performance parameters is the conventional practice for testing analog/RF devices. While this approach is highly accurate, it often incurs extremely high testing costs. A promising approach is to adopt alternate test strategy, i.e. a strategy in which test results are derived from indirect low-cost measurements. The underlying idea is to learn during a training phase the mapping between indirect measurements and circuit performance parameters, and to use only indirect measurements to predict device specifications during production testing. Despite the substantial test cost reduction offered by this strategy, its deployment in industry is today limited, mainly because confidence in alternate test predictions is difficult to assess. In this paper, we propose a novel implementation with the objective to improve confidence in alternate test predictions. The idea is to exploit model redundancy in order to identify, during the production testing phase, devices with suspect predictions and remove these devices from the alternate test flow. This approach is illustrated on a real case study for which we have experimental measurements on a set of 10,000 devices.