Microsoft AB-210 Accelerating Sales Pipelines with AI in Dynamics 365 Exam Practice Test

Parameter

Option

Entity

Opportunity

Date field

Actual Close Date

Revenue field

Actual Revenue

The correct table is Opportunity because the goal measurement is based on deal amounts when opportunities are closed. In Dynamics 365 Sales goal metrics, rollup fields define which table and source attribute are used to calculate actual or in-progress goal values. Microsoft’s goal metric documentation gives the direct example of using the Opportunity table with the opportunity actual value/source attribute to track actual money amounts from opportunities closed as Won .

The correct date field is Actual Close Date because the goal period must be determined by when the deal was actually closed, not when the opportunity was created. Microsoft’s goal metric configuration guidance states that the Date Field determines the goal period that records roll up into, and gives Actual Close Date as the relevant example.

The correct revenue field is Actual Revenue because the requirement is based on the total deal amount upon closing the opportunity. Budget Amount and Total Amount do not represent the final closed deal value for goal actuals. Goal rollup fields calculate actual performance values for completed measurable results, which fits closed opportunity revenue.

References/topics: Dynamics 365 Sales goals; goal metrics; rollup fields; opportunity actual revenue; actual close date; sales performance tracking.

The correct configuration is to enable the Microsoft Teams dialer . Microsoft’s Dynamics 365 Sales documentation states that Microsoft Teams dialer brings Teams calling directly into Dynamics 365 Sales so sellers can make and receive calls without leaving the application. The documented setup path is to go to Sales Hub app settings, open Teams calls , and turn on Teams calls for the selected security roles and apps.

This matches the requirement precisely: the sales team must place outbound calls directly from Dynamics 365 Sales records using Microsoft Teams. Phone numbers and PSTN connectivity are prerequisites for Teams calling, but option A is not the Dynamics 365 Sales feature that enables record-level calling. The mobile app is irrelevant because the question asks for calling from Dynamics 365 Sales records, not mobile access. Teams meeting integration supports meetings and collaboration, but it does not enable click-to-call or Teams dialer calling from Sales records. Therefore, the required feature is Microsoft Teams dialer .

References/topics: Microsoft Teams dialer; Teams calls in Dynamics 365 Sales; outbound calling from Sales records; Sales Hub Teams calls configuration.

The minimum dataset for a predictive lead scoring model is 40 qualified leads and 40 disqualified leads . Microsoft’s Dynamics 365 Sales documentation states that an organization must have created and closed at least 40 qualified and 40 disqualified leads within the selected training period to train the model. The broader lead and opportunity scoring documentation confirms the same threshold and distinguishes lead scoring from opportunity scoring: lead scoring requires qualified and disqualified leads, while opportunity scoring requires won and lost opportunities.

This distinction is critical. Predictive lead scoring predicts the likelihood that a lead will qualify or convert based on historical lead outcomes, so the model needs examples of both successful and unsuccessful lead qualification results. Won and lost opportunities are not the correct training records for lead scoring; they apply to predictive opportunity scoring. The question also states that the company has historical sales records for three years, but the model requirement is based on the minimum qualifying lead dataset, not merely the total age of available data. Therefore, the required minimum is 40 qualified leads and 40 disqualified leads .

References/topics: Predictive lead scoring; lead scoring model prerequisites; qualified leads; disqualified leads; AI-driven lead prioritization.

The correct action is to select a valid price list on the opportunity and verify that the product has a matching price list item for the selected unit. Dynamics 365 Sales calculates Price Per Unit from the price list item when an existing catalog product is added to an opportunity, quote, order, or invoice. Microsoft states that if an existing product is associated with a price list, the price per unit is calculated from the related price list item; otherwise, it is set to 0 .

The failure pattern described in the question is classic missing or mismatched price-list setup. Price calculation will not occur when no price list is selected, when the selected price list does not contain the product, or when the required price list item is missing. Microsoft also notes that price lists tie the product, unit, and pricing details together. Option A is incomplete because matching currency alone does not create the product/unit price list item. Option B is wrong because User Provided revenue bypasses system calculation. Option D handles discounts only; it does not fix a zero base unit price.

References/topics: Opportunity products; price lists; price list items; Price Per Unit calculation; system-calculated opportunity revenue.

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.

Requirement

Component

Record engagement is pending, completed, or failed

Sales Agent Runs table view

Weekly performance view of the Sales Close Agent in Research mode

Insights and metrics for the Sales Close Agent

For a specific opportunity that stopped receiving automated outreach, use the Sales Agent Runs table view . Microsoft states that to resolve the engagement status of a specific record, administrators can go to the sales agent run record view and filter the Regarding column by the record of interest. The Status field shows whether the engagement is Active , Failure , or Completed ; Active means pending, Failure means the engagement failed, and Completed means it finished successfully.

For the manager’s weekly performance requirement, use Insights and metrics for the Sales Close Agent . That is the performance reporting surface for the agent, not the per-record run table. The requirement specifically mentions a weekly performance view plus Last refreshed and a time period filter , which maps to the agent’s insights dashboard/metrics area. The Sales Agent Runs view is operational monitoring for individual records, while the insights and metrics view is for aggregate performance analysis such as engagement outcomes, response rates, and trend monitoring. Do not select Engage settings because settings configure how the agent behaves; they do not provide performance metrics. Opportunity pipeline view helps sellers manage opportunities, but it is not the agent performance monitoring dashboard.

References/topics: Sales Close Agent monitoring; Sales Agent Runs table; engagement status; Sales Close Agent insights and metrics; Research mode performance reporting.

The solution does not meet the goal because a billing policy for the Dynamics 365 Sales environment is associated with the pay-as-you-go consumption model, not the prepaid-credit model required in the question. Microsoft separates Dynamics 365 Sales Copilot and agent billing into two models: prepaid capacity and pay-as-you-go . Prepaid capacity uses Copilot Studio message pack subscriptions that are purchased in advance, while pay-as-you-go charges for the actual number of messages consumed by agents during the month.

For prepaid AI credits, the correct configuration is to purchase the Copilot message pack subscription and then assign that prepaid capacity to the required Power Platform/Dynamics 365 Sales environment. Microsoft’s setup steps for prepaid capacity specifically state that admins must purchase Copilot message packs through the Microsoft 365 admin center or Partner Center and assign prepaid capacity to the Power Platform environment through the Power Platform admin center.

So the proposed solution is wrong for the stated requirement. It may support monthly tracked consumption under pay-as-you-go, but it does not satisfy the explicit requirement to use pre-paid credits .

References/topics: Dynamics 365 Sales consumption-based billing; prepaid capacity; Copilot Studio message packs; Power Platform environment capacity assignment; pay-as-you-go billing policy.

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.

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.

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.

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.

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

Blueprints

Name the blueprint workspace and pin the workspace.

Questions

Create a business function with seeded prompts.

For Blueprints, the correct configuration is to name the blueprint workspace and pin the workspace. The requirement is not asking to regenerate analysis; it asks for a reliable way to return to frequently used research output. In Sales Research Agent, a generated blueprint exists inside a workspace, and Microsoft states that the workspace displays the workspace name, can be renamed, and can be switched from the workspace menu. That makes naming and pinning the workspace the correct operational pattern for reusable research access.

For Questions, the correct configuration is to create a business function with seeded prompts. Sales Research Agent business functions are designed to tailor research to a specific use case, and administrators can add starter prompts. Microsoft explains that starter prompts appear on the Sales Research Agent home page for quick access and that the full prompt is sent to the agent when selected. This directly satisfies the requirement to retain a reusable library of approved questions.

Do not select regenerate the blueprint because regeneration refreshes or recreates analysis, but it does not create a persistent shortcut to frequently used research. Do not select custom table or manual prompt questions in Sales Copilot because the native Sales Research Agent feature already supports reusable business-function prompts.

References/topics: Sales Research Agent; research blueprints; workspace management; business functions; seeded/starter prompts; 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.