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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Extend and enhance sales solutions with AI | 10-15% | - Implement collaboration features - Deploy and manage AI agents - Monitor and optimize AI performance - Configure reporting and visualization |
| Topic 2: Qualify and prioritize leads by using AI | 15-20% | - Implement Sales Qualification Agent - Configure lead qualification processes - Automate lead routing and prioritization - Fine-tune predictive lead scoring models |
| Topic 3: Develop deals by using intelligent opportunity research | 25-30% | - Configure forecasting and goal management - Deploy and configure Sales Close Agent - Use AI for opportunity research and insights - Optimize opportunity management workflows |
| Topic 4: Optimize AI-driven sales workflows and insights | 20-25% | - Set up conversational intelligence - Configure Copilot for Sales - Configure Sales accelerator and work distribution - Implement predictive scoring and relationship intelligence |
| Topic 5: Configure Dynamics 365 Sales core features for AI | 15-20% | - Configure security and access for AI features - Set up core sales entities and relationships - Design and configure sales data model - Integrate Microsoft Power Platform components |
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NEW QUESTION # 83
An organization wants Copilot to reduce time spent searching customer information before sales calls. Which capability best addresses this requirement?
Answer: C
Explanation:
Context-aware summarization allows Copilot to consolidate relevant CRM information, communications, activities, and opportunity details into a concise overview. Sellers spend less time navigating records and more time preparing for meaningful customer conversations.
NEW QUESTION # 84
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You are deploying AI agents for a company using Microsoft Dynamics 365 Sales.
The company wants to ensure that AI usage is tracked and billed monthly.
The company requires that AI billing uses pre-paid credits.
You need to configure the billing based on the requirements.
Solution: Create an Azure subscription.
Does the solution meet the goal?
Answer: A
Explanation:
The solution does not meet the goal. Creating an Azure subscription is associated with pay-as-you-go billing scenarios, not the required pre-paid credits model. Microsoft's Dynamics 365 Sales agent billing guidance states that Sales Copilot and agent capabilities support two billing models: prepaid capacity and pay-as-you- go . Prepaid capacity uses Copilot Studio message pack subscriptions purchased in advance, while pay-as- you-go bills for actual message consumption during the month.
Because the requirement explicitly says AI billing must use pre-paid credits , the correct approach is to purchase Copilot Studio message packs or Copilot Credits and assign/manage that capacity for the relevant environment. Power Platform guidance explains that Copilot Studio credits/capacity are managed and monitored through the Power Platform admin center, and if prepaid capacity is exhausted, admins must reallocate capacity, purchase more credits, or enable pay-as-you-go. An Azure subscription may be required for pay-as-you-go cost management, but it does not satisfy a prepaid-credit requirement by itself. Therefore, the proposed solution is incorrect.
References/topics: Dynamics 365 Sales agent billing; prepaid capacity; Copilot Studio message packs; Copilot Credits; pay-as-you-go billing.
NEW QUESTION # 85
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: D
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 # 86
You are responsible for improving lead prioritization in Dynamics 365 Sales.
Sales managers report that predictive scores do not match current conversion trends.
You need to edit the predictive lead scoring model to improve accuracy based on recent lead data.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
The correct sequence starts by opening and configuring the predictive scoring settings for lead scoring. The administrator must access the predictive lead scoring configuration page before changing the model.
Microsoft's predictive scoring configuration guidance states that the Predictive lead scoring page is where administrators configure model settings, including model criteria and field behavior.
Next, the administrator should edit model attributes . This is the actual model-tuning step. Microsoft states that Edit model is used to update or add fields that affect the prediction accuracy score, especially when the organization wants the model to consider different fields or a unique business process.
After modifying the attributes, the model must be retrained . Retraining is required when prediction accuracy does not meet expectations or when recent data should be incorporated. Microsoft explains that retraining uses the latest leads in the organization so the model can provide more accurate scores.
Finally, the administrator must verify predictive score accuracy by reviewing the model accuracy and performance. Exporting all lead records is unnecessary, and editing health score grading applies to opportunity health scoring, not predictive lead scoring.
References/topics: Predictive lead scoring; model attributes; model retraining; scoring accuracy; lead prioritization in Dynamics 365 Sales.
NEW QUESTION # 87
Case Study 2 - Contoso Ltd
Background
Contoso Ltd. is a global technology distributor headquartered in Redmond, Washington. The company sells networking equipment, cloud infrastructure, and managed services to enterprise customers across North America and Europe.
Contoso Ltd. currently manages its sales operations by using Microsoft Dynamics 365 Sales and plans to enable Sales Insights in the future.
The leadership team wants to modernize the sales organization by introducing capabilities that help sellers prioritize leads, analyze opportunities, and accelerate deal closure.
The company has approximately 150 field sellers, 40 insider sellers, 20 sales managers, and a central IT team responsible for configuring the sales platform. Contoso Ltd.'s executive leadership has defined the following goals for the new sales platform implementation:
Improve seller productivity by reducing manual research and data entry.
Use Al insights to prioritize leads and opportunities.
Provide automated recommendations to sales representatives throughout the deal lifecycle.
Improve collaboration between sellers and sales managers.
Current Environment:
Sales platform
Contoso Ltd. uses Microsoft Dynamics 365 Sales integrated with Microsoft 365 services.
The following components are currently deployed:
Data and lead management
Leads are important for driving new business at Contoso Ltd. They are generated from multiple sources including:
- Marketing automation campaigns
- Website registrations
- Partner referrals
- Trade show contact imports
Marketing teams import lead lists weekly into Dynamics 365 Sales by using spreadsheet imports.
Sellers currently evaluate leads manually.
Sales managers report that this process results in inconsistent prioritization across regions.
Opportunity management
Opportunities are tracked in Dynamics 365 Sales through a defined qualification-to-opportunity lifecycle to ensure accurate reporting.
Sales leadership has requested the Contoso Ltd. IT team to evaluate the following capabilities:
- AI-generated summaries of opportunity records.
- AI-driven research for customer organizations.
- Automated recommendations for closing deals.
Business Requirement:
Contoso Ltd.'s executives have defined the following business requirements:
Lead management
Inside sellers must use a seller engagement workspace that prioritizes leads automatically based on predicted likelihood to convert.
All sellers are guided through a consistent set of qualification steps aligned to Contoso Ltd.'s sales strategy.
Leads cannot be advanced to the development stage until a decision maker has been confirmed.
Sales leadership requires flexibility in how leads are converted. Inside sales need automatic opportunity creation. Field sellers need the option to manually decide whether an opportunity should be created when a lead is qualified.
Lead scoring must be implemented to help sellers identify high potential leads automatically.
Lead scoring must continuously improve based on historical sales outcomes.
Opportunity development
The opportunity flow must be improved by using Al-driven research and automated insights.
Field sellers must be able to access contextual research about customers and competitors while working on opportunities.
Opportunity records must display Al-generated summaries to reduce the time required to review historical activities.
Field sellers must use the Opportunity pipeline view to visualize, prioritize, and manage opportunities.
All opportunity revenue must be calculated using opportunity line items based on selected Contoso Ltd.'s products and standard pricing.
Sales managers must receive an external quarterly competitive intelligence brief to help with their research. This should be delivered as a PDF document.
Sales performance
Sales managers require improved pipeline and revenue forecasting capabilities. The managers need to report on their business performance and use insights, visuals, and recommended next actions.
The new solution must provide the following:
- Allow sales managers to configure forecasts for opportunity pipelines.
- Notify the seller's manager when a draft quote is created and requires approval.
- Allow leadership to track performance against revenue goals.
- Ask questions about Dynamics 365 Sales data to support executive analysis and decision making.
- Support data definitions used at Contoso Ltd. when reporting.
Technical Requirement:
Contoso Ltd.'s IT department defines the following priorities for the sales platform.
AI capabilities
The platform must support the following Al features:
- Lead scoring to guide sellers to priority items.
- Opportunity research insights to prepare deal analysis.
- Copilot-generated summaries of sales records to help with decision making.
- AI-powered research and analytics to aid leadership reporting.
- When performing sales research, sales managers must be able to save their favorite prompts.
Automation
Automation must be implemented to guide sellers through recommended next steps during the sales cycle.
Any approvals must use the Power Automate Approvals connector.
Field sellers use the existing Lead to Opportunity Business Process Flow to manage their leads.
A Power Automate desktop flow is used to process orders into a partner ordering application that completes fulfillment.
Collaboration
All sellers must be able to collaborate by using Teams and store related sales documents in SharePoint.
AI-driven insights
The company is deploying the Sales accelerator to guide seller activities.
The Sales Research Agent
Sales managers will use the Sales Research Agent to gather intelligence about customer organizations.
The sales managers have the following requirements:
- Must be able to reuse visualizations after they are created; they don't want to recreate them.
- Must have a consistent method to quickly return to a blueprint that they frequently use.
- Must be able to retain a library of questions to receive consistent responses based on definitions approved by the company.
- Should have Bing search enabled on the environment for generative Al feature settings.
Platform extensions
The IT department plan to extend the platform by doing the following:
- Create Power Automate flows to automate seller tasks.
- Embed Power Apps components inside model-driven forms.
- Embed Power BI reports for sales analytics.
Testing
The IT department is testing predictive lead scoring with a small group of sellers.
The IT department edits the existing lead scoring model by adding new fields.
Issues:
Users have reported the following issues:
- Field sellers spend a significant amount of time researching customers before engaging with prospects.
- Sales managers cannot easily identify the most promising opportunities across regions.
- Lead prioritization varies significantly between sales teams.
- Testers report they cannot access the field changes in the lead score.
- A sales manager generated a blueprint but forgot the competitive intelligence brief.
You need to resolve the competitive intelligence brief issue for the sales manager.
What three actions should you do? Each correct answer presents part of the solution. Choose three.
Answer: A,C,D
Explanation:
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.
NEW QUESTION # 88
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