The Accelerating Sales Pipelines with AI in Dynamics 365 (AB-210) certification exam is one of the hottest and most industrial-recognized credentials that has been inspiring beginners and experienced professionals since its beginning. With the Accelerating Sales Pipelines with AI in Dynamics 365 (AB-210) certification exam successful candidates can gain a range of benefits which include career advancement, higher earning potential, industrial recognition of skills and job security, and more career personal and professional growth.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Extend and enhance Sales | 10–15% | - Extending Dynamics 365 Sales with AI capabilities - Integration with Microsoft Copilot and Power Platform |
| Topic 2: Qualify and prioritize leads using AI | 15–20% | - Lead scoring and qualification models - AI-driven lead prioritization workflows |
| Topic 3: Develop deals using intelligent opportunity research | 25–30% | - AI-assisted deal progression strategies - Opportunity insights and forecasting |
| Topic 4: Optimize AI-driven sales | 20–25% | - Copilot and AI assistant capabilities in Sales - Sales productivity and automation enhancement |
| Topic 5: Configure Dynamics 365 Sales core features for AI | 15–20% | - Sales environment configuration - Data and process setup for AI features |
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NEW QUESTION # 62
A sales administrator wants AI predictions to improve over time as additional closed opportunities become available. Which statement accurately describes predictive models in Dynamics 365 Sales?
Answer: A
Explanation:
Predictive AI models continuously learn from an organization's historical opportunity outcomes and available CRM data. As more opportunities close successfully or unsuccessfully, the model can improve prediction quality by identifying additional patterns associated with sales success.
NEW QUESTION # 63
You are implementing an agent in Dynamics 365 Sales for a company.
The customer runs weekly webinars that generate thousands of inbound leads.
The sales team needs to automate the first part of qualification but transfer promising leads to sellers.
The marketing team needs to analyze lead information and determine qualification possibility. However, all outreach to prospects must be managed by human sellers due to compliance requirements.
You have the following requirements for the agent:
. The agent must research inbound leads and if they match the target customer profile.
. The agent must evaluate Budget, Authority, Need, and Timeline during early engagement before transferring to sellers.
. The agent must send the initial outreach email automatically, manage responses, and detect positive intent.
. The leads that do NOT match the target customer profile should be disqualified automatically, and sales supervisors should be notified.
. The qualified and positive intent leads should be transferred to sellers.
You need to determine which configuration should be used.
Which agent and mode should you use?
Answer: C
Explanation:
The correct configuration is Sales Qualification Agent in Research and engage mode . The question contains one conflicting sentence about human-managed outreach, but the explicit agent requirements require autonomous outreach, response handling, BANT evaluation, positive-intent detection, disqualification of poor- fit leads, supervisor notification, and seller handoff. Those capabilities map only to Research and engage mode.
Microsoft's Sales Qualification Agent documentation states that both modes can research leads and check target customer profile fit, but only Research and engage mode checks BANT criteria, sends outreach emails, detects positive intent, sends follow-up emails, and engages with leads based on their responses.
Microsoft's handoff criteria documentation also states that BANT criteria are only used by Research and engage mode, and that leads satisfying BANT and purchase-interest criteria are handed over to sellers, while leads that do not satisfy the configured criteria are disqualified and added for supervisor review.
Option B or D would be correct only if the controlling requirement were strictly "human sellers must manage all outreach." But the listed automation requirements go beyond Research-only mode, so the exam answer is C
.
References/topics: Sales Qualification Agent; Research and engage mode; target customer profile; BANT; purchase intent; lead handoff; supervisor review.
NEW QUESTION # 64
You are creating a pricing list in Dynamics 365 Sales. All prices must end in $.98.
You need to select the function that establishes this pricing requirement.
What should you use?
Answer: D
Explanation:
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.
NEW QUESTION # 65
A company plans to implement Dynamics 365 Sales and wants to design a Dataverse data model that supports AI-driven insights across the sales lifecycle.
The company must be able to track the progression of a deal from initial prospect identification through to finalized billing.
The data model must support the following requirements:
. Prospects must be captured before they become qualified sales deals.
. Qualified deals must support forecasting and pipeline analysis.
. Sales teams must be able to generate pricing proposals for customers.
. Accepted proposals must transition into fulfillment records and then final billing records.
You need to design the Dataverse table model that supports the required sales lifecycle.
Which table relationship should you implement?
Answer: E
Explanation:
The correct sales lifecycle is Lead # Opportunity # Quote # Order # Invoice . In Dynamics 365 Sales, a lead captures an initial prospect before the person or organization is qualified. Microsoft's sales process guidance explains that sellers generally work with leads during the qualifying stage, and the goal is to qualify and advance the lead to the next stage of the sales process. Once qualified, the deal becomes an opportunity , which is the record used for pipeline tracking, revenue estimation, sales stages, probability, and forecasting analysis. Microsoft's prospect-to-quote guidance states that Dynamics 365 Sales supports capturing, managing, scoring, and converting leads into opportunities when qualification criteria are met.
Quotes are then created from opportunities to present pricing proposals to customers. Microsoft states that quotes created from opportunities are prefilled with opportunity details and products. Once the proposal is accepted, the quote can be converted into a sales order for fulfillment and then into an invoice for billing; Microsoft's quote/order/invoice process sample explicitly demonstrates converting an opportunity to a quote, a quote to a sales order, and a sales order to an invoice. Therefore, option B is the only sequence that correctly supports the full sales lifecycle.
References/topics: Lead management; opportunity pipeline; quote creation; order fulfillment; invoice billing; Dataverse Sales tables.
NEW QUESTION # 66
A seller notices that an opportunity score increased significantly after several customer meetings and email exchanges. Which AI capability most likely influenced the updated prediction?
Answer: C
Explanation:
Opportunity scoring evaluates multiple relationship signals such as meeting frequency, email engagement, customer responsiveness, and historical behaviors. Increased positive interactions often improve opportunity health and raise the predicted likelihood of successfully closing the deal.
NEW QUESTION # 67
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