AB-210 Questions and Answers

The correct outcome is that the agent disqualifies the lead, notifies the supervisor, and the lead continues to be assigned to the agent . In Research-only mode, the Sales Qualification Agent evaluates whether the lead fits the target customer profile. Microsoft states that when target customer profile suitability is high or medium , the lead is handed over to a seller according to the configured assignment rule. When target customer profile suitability is low , the agent disqualifies the lead, notifies the supervisor, and the lead remains assigned to the agent.

Option A is wrong because Research-only mode does not always transfer every lead to a seller; low-fit leads can be disqualified. Option C describes Research and engage behavior, where the agent sends outreach, manages responses, and hands over leads when positive intent is detected. Research-only mode generates outreach drafts but does not send outreach emails or engage with leads. Option D is also wrong because BANT criteria are only used by Research and engage mode, not Research-only mode. Therefore, a low target customer profile suitability result in Research-only mode results in agent disqualification and supervisor notification.

References/topics: Sales Qualification Agent; Research-only mode; target customer profile suitability; lead disqualification; supervisor notification; lead handoff rules.

The correct answer is Rounding Amount . In Dynamics 365 Sales price list item configuration, rounding is used when calculated prices must follow a specific price-ending convention, such as ending in .98 or .99 . Microsoft’s pricing guidance explains that price list items can use rounding behavior so calculated product prices can be rounded and made to end in a specified amount, such as 99 cents. The value that defines the exact ending amount is the Rounding Amount . Therefore, to make all calculated prices end in $.98 , the administrator would configure the rounding amount as 0.98 .

Rounding Policy is not precise enough by itself. It controls the direction of rounding, such as rounding up, down, or to the nearest value, but it does not define the exact cents-ending requirement. Pricing Method determines how the price is calculated, such as a currency amount, percentage of list price, markup, or margin. Percentage is used when a percentage-based pricing method is selected, but it does not force prices to end in $.98 . The requirement is explicitly about the final rounded price ending, so Rounding Amount is the correct function.

References/topics: Price lists; price list items; rounding amount; rounding policy; Dynamics 365 Sales product pricing.

Requirement

Tool

Perform purchase required by CFO

Microsoft 365 admin center

Configure Sandbox billing

Power Platform admin center

Configure Production billing

Microsoft 365 admin center

Why: Production requires reliable AI capacity so sellers are not blocked when prepaid Copilot capacity is depleted. The correct production setup is pay-as-you-go / billing policy configuration through the Microsoft 365 admin center . Azure portal is not the best answer here because the scenario says no Azure subscriptions are available , and Microsoft 365 admin center can be used for the billing policy workflow. Microsoft Agent 365 is for agent management/governance, not billing configuration.

For Dynamics 365 Sales predictive lead scoring, the model cannot be created unless there is enough historical lead outcome data in the selected training window. Microsoft’s predictive lead scoring configuration requirement states that the organization must have at least 40 qualified leads and 40 disqualified leads in the selected training period. The official guidance specifically explains that if the model is trained with leads from the past three months, the organization needs “at least 40 qualified and 40 disqualified leads” created and closed during that period.

In the sandbox, AdventureWorks has 30 qualified leads and 20 disqualified leads. However, 10 of the qualified leads are older than three months, and the business requirement says predictive scoring must be enabled for leads created within the last three months. Therefore, only 20 qualified leads are usable for this model. The sandbox currently has 20 usable qualified leads and 20 disqualified leads. To meet the required threshold of 40 each, the company needs 20 more qualified leads and 20 more disqualified leads . Simply creating 40 new leads is not enough because predictive scoring requires outcome-classified leads, not merely open or newly created records.

References/topics: Configure predictive lead scoring; lead scoring model training data requirements; AI-driven lead prioritization in Dynamics 365 Sales.

Requirement

Component

Mountaineer range

Product family

Athlete package

Product bundle

Metal range

Product property

The Mountaineer range should be configured as a Product family because it represents a product classification hierarchy. Dynamics 365 Sales uses product families to organize related products, child products, and bundles under a parent structure. Microsoft’s product catalog guidance states that product families can contain child product families, products, and bundles in a hierarchy, which fits the mountaineer cycle range with economy and premium fitting variations.

The Athlete package should be configured as a Product bundle because it combines multiple sellable items: a cycle, connected devices, and subscription-based analytics services. A bundle is used when multiple products are sold together as one commercial package. Dynamics 365 Sales allows products to be associated with a bundle, including quantities and required or optional components.

The Metal range should be configured as a Product property because sellers must choose a configurable attribute—chrome, aluminum, or titanium—when adding the mountaineer cycle to an opportunity. Product properties define product characteristics such as size, color, or component, and can be used at run time when adding products to opportunities, quotes, orders, or invoices.

References/topics: Product catalog setup; product families; product bundles; product properties; opportunity product configuration.

The required fix is about visibility and security , not agent operating mode. In the scenario, production has EnableRoleBasedSystemViews set to true, which restricts system views based on role configuration. Because the business requirement says all sellers should be able to access disqualified leads , the role-based system view setting must be updated so sellers can access the view that exposes leads disqualified by the Sales Qualification Agent.

The second required action is to change Lead table permissions. The customized Salesperson role currently grants sellers read access only to leads they own. That is too restrictive because leads disqualified by the Sales Qualification Agent may not be owned by each seller. To let all sellers view those records, sellers need organization-level Read permission on the Lead table. Option A is wrong because upgrading the agent to research and engage mode affects how the agent researches and interacts with leads; it does not solve seller access to disqualified records. Option C is also wrong because the look-back period controls which historical leads the agent evaluates, not who can view disqualified leads.

References/topics: Sales Qualification Agent; role-based system views; Dataverse security roles; Lead table read access; AI-driven lead qualification.

The issue is that the sales manager already generated a Sales Research Agent blueprint but forgot to include the quarterly competitive intelligence brief. The brief is an external document and the case study states it is delivered as a PDF document , so the correct first content action is to upload the competitive intelligence brief as a PDF file , not CSV. Microsoft’s Sales Research Agent documentation states that users can select Add data to upload files including CSV, Excel, or PDF to augment research with additional data.

The second required action is to add a data source , because the competitive intelligence brief is not part of the default Dynamics 365 Sales data. Sales Research Agent connects to Dynamics 365 Sales data by default, but additional external files must be added as supporting data sources before they can influence the analysis. The documentation identifies the Data area as the place where the research data source is specified and where additional data sources can be added.

The final action is to regenerate the canvas/blueprint so the agent reprocesses the analysis using the newly added PDF. Refreshing alone is not the best answer because the problem is not stale data; it is missing context. Regeneration creates updated research output with the new source included.

References/topics: Sales Research Agent; research canvas; adding PDF data sources; blueprint regeneration; intelligent opportunity research.

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.