The correct action is to assign AI capacity to the Dynamics 365 environment . AI agents and AI-powered features run inside a Power Platform/Dynamics 365 environment, and the environment must have available AI capacity or credits before those features can execute reliably. Microsoft's AI capacity guidance states that once acquired, AI credits can be allocated or assigned to a specific environment, and when an environment has access to the credits, AI features are enabled in that environment. It also states that if consumption exceeds available credits, some features may be blocked.
Option A is wrong because creating a new environment does not automatically provide AI capacity. The existing Dynamics 365 Sales environment can consume AI credits only when capacity is assigned or available to it. Option C is also wrong because Copilot Studio author permissions allow users to design or create agents; that is an authoring permission, not a capacity configuration. Option D is useful after deployment because administrators can monitor consumption in the Power Platform admin center, but monitoring does not make credits available. Microsoft also notes that AI Builder capacity is licensed as an add-on that must be allocated to a Power Platform environment by an administrator.
References/topics: AI capacity allocation; Copilot/AI credits; Power Platform environment capacity; Dynamics 365 Sales AI agents; agent deployment prerequisites.

Explanation:
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.

Explanation:
Requirement
Configuration
Define pricing for products
Price list item
Automatically use a localized price list
Territory
Group product prices for each region
Price list
The correct component for defining product-specific prices is the Price list item . In Dynamics 365 Sales, price list items connect a product, unit, pricing method, and amount to a specific price list. Microsoft's product pricing guidance states that price lists and price list items are used to define product pricing, and price list items are where the per-product pricing details are configured.
To automatically use a localized price list, configure Territory . Dynamics 365 Sales can automatically select a default price list for opportunities, quotes, orders, and invoices based on the sales territory of the user.
Microsoft states that a price list can be associated with a territory by using the Territory Default Pricelist connection role, allowing users in that territory to have the correct price list selected by default.
To group product prices for each region, use a Price list . A price list is the container that groups the prices charged for products or services. Microsoft guidance explains that organizations can create multiple price lists to maintain separate pricing structures for different regions, sales channels, or territories. Currency supports monetary localization, but it does not group regional product prices by itself. Product bundle is unrelated because it combines products for sale as a package, not regional pricing.
References/topics: Product catalog pricing; price lists; price list items; territory-based default price lists; regional pricing configuration.

The correct agent type is Sales Qualification Agent: Research and engage . The requirement is specifically about inbound lead processing: researching incoming leads, generating outreach, autonomously contacting leads, following up, and evaluating qualification during the engagement process. Microsoft defines Sales Qualification Agent as the AI agent used to automate lead qualification in Dynamics 365 Sales, especially for high-volume inbound leads from sources such as websites, events, and webinars. In Research and engage mode, the agent researches the lead, evaluates fit using target customer profile and BANT criteria, sends personalized outreach emails, engages based on responses, sends follow-up emails, detects positive intent, and then hands promising leads to sellers.
Research-only mode is not enough because it generates a draft outreach email for seller review but does not send outreach emails or detect positive intent from responses. Microsoft's mode comparison explicitly shows that BANT checking, sending outreach, detecting positive intent, and follow-up emails are available in Research and engage mode, not Research-only mode. Sales Close Agent options are wrong because they apply to opportunity/deal execution, not initial lead qualification.
References/topics: Sales Qualification Agent; Research and engage mode; lead research; outreach automation; BANT qualification; AI-driven lead prioritization.

The correct scenarios are B and C . Sales Research Agent is designed for research and analysis, not autonomous record updates or email engagement. Microsoft describes the agent as an AI-powered research canvas that lets users solve complex business questions through natural language, generate research blueprints, review sales-data insights, and ask follow-up questions to refine the analysis. It can use Dynamics
365 Sales data, uploaded offline files such as CSV, Excel, or PDF, and connected data sources to support pipeline exploration and sales operations analysis.
Option B is correct because Microsoft states that Sales Research Agent may use Bing Search only when the Bing search consent setting is enabled. When Bing search is disabled, the agent operates only on internal data sources that the user already has permission to access. Option C is also correct because the agent generates a research blueprint with visualizations and a text overview, and users can continue iterating by asking follow- up questions or changing visualizations on the canvas. Option A is wrong because Sales Research Agent is analytical; it does not update opportunities with risk or importance values. Option D is wrong because automatic email engagement is the domain of sales/qualification engagement agents, not Sales Research Agent.
References/topics: Sales Research Agent; research canvas; AI-generated blueprints; Bing search consent; internal sales data analysis; intelligent opportunity research.