解决大规模广告拦截中的不确定性建模难题，提升反欺诈效果。

Real-Time Auction (RTA) interception decides which incoming advertising requests reach downstream systems, and therefore controls the quality of the data those systems learn from. At JD.com, off-site advertising produces on the order of hundreds of billions of requests per day, and the RTA channel alone serves up to hundreds of millions of requests per minute. Filtering low-quality and fraudulent traffic at this scale requires estimating each request's value with calibrated confidence, which we treat as an uncertainty modeling problem. Two obstacles stand in the way. First, advertising labels are severely imbalanced: deals are rare, and we show both analytically and empirically that standard weight-based uncertainty degrades under such sparsity, collapsing onto predicted probability and adding no signal. Second, methods such as SWAG and Bayesian neural networks require multiple stochastic forward passes per request, making full-traffic scoring prohibitively expensive. We address both problems with UMDA, a multi-objective framework that shares uncertainty across funnel-correlated objectives, using the reliable uncertainty of a balanced metric to compensate for the degenerate uncertainty of sparse ones. We then distill the multi-pass teacher into a single-pass student that reproduces both aleatoric and epistemic uncertainty at roughly one-tenth of the inference cost. On JD e-commerce dataset and the public Criteo dataset, UMDA supplies more effective samples to downstream tasks, and the distilled student preserves this capability. In production, it scores the full traffic in a near-line pipeline that feeds an hourly blacklist for online interception; a seven-day A/B test on 5% of live traffic cuts the click fraud rate by 3.59% and raises CVR by 4.01% at a matched interception ratio while leaving converted users essentially unchanged, and the model has since been deployed to full traffic.