You need to configure AI capabilities that prioritize leads and improve scoring accuracy in Dynamics...

Requirement

Configuration

Automatically prioritize leads based on conversion probability

Predictive lead scoring

Ensure the lead scoring improvement requirement is met

Automatically retrain model

The correct capability for automatically prioritizing leads based on conversion probability is Predictive lead scoring . Dynamics 365 Sales predictive lead scoring assigns scores to leads so sellers can prioritize the leads most likely to qualify or convert. Microsoft’s Sales documentation states that predictive lead scoring helps sales teams prioritize leads based on scores and improve lead qualification outcomes. This directly matches Contoso’s requirement to guide sellers toward high-potential leads and reduce inconsistent manual prioritization across regions.

To meet the requirement that lead scoring must continuously improve based on historical sales outcomes, configure the model to automatically retrain . Microsoft’s predictive lead scoring guidance explains that retraining uses the latest leads in the organization to improve model accuracy, and automatic retraining allows the application to retrain the model every 15 days. This is the correct option because Contoso wants the scoring model to improve continuously as more closed lead outcome data becomes available.

“Lead scoring model” is too generic; the AI capability required is specifically predictive lead scoring. “Relationship intelligence” supports relationship analytics and engagement insights, but it does not score leads by conversion probability. “Configure forecast” applies to pipeline forecasting, not lead scoring accuracy.

References/topics: Predictive lead scoring; lead prioritization; scoring model accuracy; automatic model retraining; AI-driven lead qualification.