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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Configure Dynamics 365 Sales core features for AI | 15–20% | - Examine Sales security model - Configure timeline and data import/export - Evaluate prerequisites for deploying Sales - Configure mailboxes and business process flows |
| Topic 2: Develop deals using intelligent opportunity research | 25–30% | - Configure and use Sales Research Agent insights - Configure Sales Close Agent and interpret actions - Optimize opportunity management and pipeline views |
| Topic 3: Qualify and prioritize leads using AI | 15–20% | - Fine-tune predictive scoring model - Configure Sales Qualification Agent and monitor its performance - Configure lead and opportunity experience |
| Topic 4: Optimize AI-driven sales | 20–25% | - Prepare and deploy AI agents in Sales - Configure predictive scoring and relationship intelligence - Set up conversational intelligence and forecasting - Design AI-first sales strategy with Copilot and agents |
| Topic 5: Extend and enhance Sales | 10–15% | - Deploy and configure Sales mobile app and Teams calling - Create Power Platform components (Power Automate, Power Apps, Power BI) |
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NEW QUESTION # 85
A company uses the Sales Qualification Agent to analyze leads and determine qualification probability.
The Sales Qualification Agent is configured in Research-only mode. The agent processes a lead and determines the lead's target customer profile suitability is Low.
You need to determine the outcome of the agent's action.
What should you infer about the agent?
Answer: C
Explanation:
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.
NEW QUESTION # 86
You manage Dynamics 365 Sales.
The sales managers want to confirm that the Sales Close Agent generates insights that help progress deals.
When monitoring the Sales Close Agent, you observe that some engagements are failing.
You need to determine how the Sales Close Agent handles engagement failures.
How should you expect the agent to behave when an error occurs?
Answer: B
Explanation:
The correct behavior is that the Sales Close Agent retries up to three times within the next hour , using an exponential backoff strategy. If those immediate attempts fail, the system continues retrying the engagement over the next three days . If the engagement still fails after that retry window, the record is marked as a failure . Microsoft's Sales Close Agent documentation explicitly describes this retry pattern for engagement errors and also identifies the possible run statuses as Active, Failure, and Completed.
Option A is wrong because the agent does not retry only once after 24 hours, and failed records are not marked Completed. Option B is wrong because the agent has a defined automatic retry cycle before failure status is finalized. Option C is also wrong because the retry behavior is bounded; it does not retry indefinitely every few minutes. The failure handling is controlled, time-limited, and designed to avoid uncontrolled AI- credit consumption while still giving transient failures a chance to recover. This matters for sales managers because failed engagements must be monitored and remediated through the Sales Agent Runs view rather than assuming every selected record was successfully progressed.
References/topics: Sales Close Agent; engagement processing; retry behavior; Sales Agent Runs monitoring; opportunity/deal progression.
NEW QUESTION # 87
A company is using AI agents in Dynamics 365 Sales.
The company requires that the agent must do the following:
Research incoming leads and generate initial outreach email.
. Autonomously contact leads, follow up, and evaluate qualification during the process.
You need to identify which AI agent in Sales supports these requirements.
Which agent type should you use?
Answer: A
Explanation:
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.
NEW QUESTION # 88
An organization wants Copilot responses to include CRM information while respecting existing security permissions. What should administrators expect?
Answer: B
Explanation:
Copilot follows existing Dynamics 365 security models and only surfaces information users are already authorized to access. This ensures AI-generated responses remain consistent with organizational security and data governance policies.
NEW QUESTION # 89
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.
Drag and Drop Question
You need to automate the quote approval process.
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 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.
NEW QUESTION # 90
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