A consultant supports a client that just completed an acquisition, roughly doubling the volume of leads flowing into Dynamics 365 Sales and introducing a new industry the client previously had little exposure to. The consultant is concerned the existing predictive lead scoring model, trained before the acquisition, will misjudge these new leads. (Select TWO.) Which two actions should the consultant recommend?

The correct recommendations are manually triggering a refresh or retrain of the predictive scoring model rather than waiting for its next scheduled run, and monitoring scores on the newly acquired industry's leads over the following weeks to confirm the retrained model reflects their real conversion patterns. A sudden, large shift in the mix of incoming leads is exactly the kind of change that can make a model trained on the old mix produce misleading scores, so triggering a manual refresh rather than waiting for the next scheduled retrain gets the model learning from the new industry's patterns sooner. Because a retrain does not guarantee an immediately perfect fit, the consultant should also recommend watching how the new industry's leads score and convert over the following weeks to confirm the model is now tracking their real outcomes, adjusting further if it is not. Disabling predictive lead scoring permanently because a single Dynamics 365 Sales environment can never score leads from more than one industry is incorrect because it is a drastic overreaction; Dynamics 365 Sales predictive scoring can absolutely learn from a lead population spanning multiple industries, it just needs enough retrained history to do so well. Deleting the pre-acquisition closed lead history so the model trains exclusively on the new industry's data is incorrect because discarding the pre-acquisition closed history throws away legitimate signal the model could still learn from, and shrinking the training data right when more history is needed works against the goal rather than helping it.