AB-210 Exam Dumps - Accelerating Sales Pipelines with AI in Dynamics 365

The correct configuration is to set related record creation to Seller. The case study states that sales leadership requires flexibility in how leads are converted: inside sales need automatic opportunity creation, but field sellers need the option to manually decide whether an opportunity should be created when a lead is qualified. In the new lead qualification experience, Dynamics 365 Sales lets administrators control whether account, contact, and opportunity records are created automatically during lead qualification or whether sellers are prompted to choose which related records to create. Microsoft’s lead qualification guidance confirms that when automatic creation is not enabled, sellers are prompted to select which records should be created during qualification.

Option B and option E are wrong for field sellers because automatic creation would remove the manual decision point required by the case study. Option C is relevant to guiding sellers through sales stages, but the existing Lead to Opportunity Business Process Flow does not control whether related records are automatically created when a lead is qualified. Option D is unrelated because Copilot summaries help sellers review record information; they do not configure lead conversion behavior. Therefore, the required configuration for field sellers is Seller-controlled related record creation.

References/topics: Lead qualification experience; lead-to-opportunity conversion; related account, contact, and opportunity creation; Dynamics 365 Sales lead management.

Create a Power Automate cloud flow.

Add a Dataverse trigger.

Define an approval step.

Test and activate the flow.

The correct automation pattern is an automated Power Automate cloud flow triggered by a Dataverse event. The case study states that the process must notify the seller’s manager when a draft quote is created and requires approval, and that approvals must use the Power Automate Approvals connector. Therefore, the flow should first be created as a cloud flow, then started by a Dataverse trigger such as when a row is added, modified, or deleted on the Quote table, filtered for the draft quote condition. Microsoft’s Dataverse trigger guidance confirms that this trigger runs when a selected Dataverse table row is created, modified, or deleted.

After the trigger is defined, the flow must include an approval step using Power Automate approvals. Microsoft’s approval workflow guidance states that Power Automate can manage approvals across services including Dynamics 365 and Dataverse-based processes. The final action is to test and activate the flow so the quote approval automation is available to users. Do not use a scheduled trigger because the requirement is event-driven, not time-based. Do not connect to the existing desktop flow because that desktop flow processes partner orders for fulfillment, not draft quote approvals. A Teams step is optional collaboration, not the required approval mechanism.

References/topics: Power Automate cloud flows; Dataverse triggers; Approvals connector; Dynamics 365 Sales quote approval automation; sales platform extension.

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.