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NEW QUESTION # 26
A company has an AI solution built by using Microsoft Copilot Studio and Power Platform. The solution is used by the company ' s sales, marketing, and customer service teams.
You are performing a return on AI investment (ROAI) analysis to evaluate the impact of the solution.
You need to identify which measurable business drivers to include in the analysis.
Which two business drivers should you identify? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answers are A. the reduced average case resolution time and D. increased employee productivity.
This question is asking for measurable business drivers for a ROAI analysis of a Copilot Studio and Power Platform solution used by operational teams.
For ROAI, the strongest business drivers are those that are:
directly attributable to the AI solution
operationally measurable
tied to business outcomes
relevant across teams
Why A is correct
Reduced average case resolution time is a strong measurable driver because it reflects a direct operational improvement in customer service and support workflows.
This metric can be quantified clearly by comparing:
baseline resolution time before AI
resolution time after deployment
That makes it ideal for ROAI because faster case resolution often leads to:
lower service cost
higher throughput
better customer experience
more efficient staffing
Why D is correct
Increased employee productivity is another core ROAI driver because AI solutions in sales, marketing, and customer service are often deployed specifically to reduce manual work and improve output per employee.
This can be measured through indicators such as:
more tasks completed per agent or employee
reduced manual effort
increased throughput
faster response cycles
more time spent on higher-value work
From an AI business solutions perspective, productivity improvement is one of the most common and valid drivers in ROAI analysis.
Why the other options are incorrect
B). market capitalization
This is too broad and influenced by many external factors. It is not a practical direct business driver for evaluating the specific impact of one AI business solution.
C). economic market predictability
This is not a direct business driver created by the solution and is too external to the organization's operational AI ROI calculation.
E). brand awareness
Brand awareness can matter strategically, but it is less directly attributable and less operationally measurable than resolution time and productivity for this kind of internal business solution.
Expert reasoning
For ROAI questions, prefer metrics that are:
operational
attributable
measurable before and after deployment
That leads to:
reduced average case resolution time
increased employee productivity
So the correct choices are:
NEW QUESTION # 27
Case Study 1 - Fabrikam, Inc
Background
Fabrikam, Inc., is a global consumer goods company that is undergoing a digital transformation initiative to migrate its entire infrastructure to the Microsoft cloud. As a key element of this cloud migration, the company will implement Microsoft Dynamics 365 Sales, moving away from the current on-premises proprietary technologies used by its business-to-business (B2B) sales team.
As part of the cloud migration, Fabrikam will adopt an AI-first approach to its business solutions and implement AI solutions, wherever possible, to streamline operations.
Problem Statements
Fabrikam's infrastructure currently relies on various on-premises systems that require sales executives to use corporate computers with physical keyboards to access business information during customer interactions. Mobile phones cannot be used for these purposes, as the systems depend on keyboard input. As a result, the sales executives spend a lot of time using keyboards to search for data on several disparate systems and file servers, rather than focusing on the customers. This affects the customer experience.
Fabrikam stakeholders are concerned that users will be hesitant to adopt AI. If the AI initiatives are NOT adopted, cost savings will never be realized. Additionally, funding for future AI initiatives will depend on demonstrating an increase in AI adoption month over month. As the AI agent initiative for the sales team will be the first for Fabrikam, the rapid adoption of the agent is a high priority.
Planned Initiatives
General
Fabrikam management has prioritized AI-driven projects to improve efficiency, customer engagement, and responsible AI adoption. The current application infrastructure is on-premises and must be migrated to the cloud to support the adoption of these technologies.
Infrastructure Migration
Fabrikam plans to migrate from its current on-premises infrastructure to a completely cloud-based topology; this will include user authentication, the security framework, and, primarily, the adoption of the services by end users.
All the data from the different systems will be consolidated into a single data source - a common data model that will use a Microsoft Dataverse environment as a single source of truth (SSOT) for the sales team.
Sales Cycle Enablement
To achieve the company's objectives, Fabrikam intends to implement the following strategies to enhance the sales cycle:
- Use low-code development to create a single AI agent that has
Dataverse as its core component.
- Ensure that sales managers can access unanswered correspondence from
prospects and intervene as appropriate.
- Replace the previous proprietary software with Dynamics 365 Sales to
track sales cycles and customer interactions.
- Have the sales executives use Dynamics 365 Sales to track
interactions for open opportunities and send follow-up communications
to prospects.
- Have the sales executives use handsfree headsets to interact with an
AI agent when they have questions about internal policies or customer
data.
Requirements
Infrastructure Migration
Fabrikam has identified the following infrastructure migration requirements:
- Azure must be used for all future infrastructure workloads.
- The company must follow Microsoft-recommended methodologies for
infrastructure migration to the cloud.
- Any created AI agents must have their return on investment (ROI)
calculated to ensure that the solution will save the company money.
Sales Cycle Enablement
Fabrikam has identified the following requirements for sales cycle enablement:
- The final AI agent must follow Microsoft recommendations for a
conversational user experience.
- A designated checklist must be reviewed to ensure that the AI agent
follows Microsoft deployment recommendations for a compliant solution.
- Detailed telemetry must be logged for the first created AI agent to
help troubleshoot and optimize the agent during the initial AI agent
adoption process.
- Unexpected AI agent actions must end in an escalation to a live
representative. For example, a sales executive must be rerouted to a
representative if the agent cannot answer a question after two failed
attempts.
- The return on investment (ROI) of switching from the current process
to the future process is required for stakeholder sign off.
- The sales team must use Dynamics 365 Sales to correspond with
prospects more quickly and efficiently than currently.
- Sales managers must report on the adoption of the AI agent to key
Fabrikam stakeholders on a monthly basis.
- Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
Hotspot Question
Which components should you use to meet the sales cycle enablement requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Scenario:
Use low-code development to create a single AI agent that has Dataverse as its core component.
Box 1: Microsoft Copilot Studio
For AI agent creation
Scenario:
A designated checklist must be reviewed to ensure that the AI agent follows Microsoft deployment recommendations for a compliant solution.
The Microsoft tool that should be used to create the AI agent is Microsoft Copilot Studio.
It is a low-code platform specifically designed for building and managing intelligent agents that integrate with Microsoft Dataverse as a core component for storing data and providing business context. To ensure the agent follows Microsoft's deployment recommendations, users should consult the implementation checklist provided within the Microsoft Copilot Studio documentation.
Key Features of Microsoft Copilot Studio:
Low-Code Interface: Uses a graphical builder or natural language to design agent behaviors and conversation flows.
Dataverse Integration: Leverages Dataverse as the enterprise data platform to ground agents in organizational knowledge and store shared context.
Governance and Compliance: Includes built-in responsible AI features and aligns with enterprise security standards.
Multi-Channel Deployment: Allows publishing agents across Microsoft Teams, websites, and mobile apps.
Box 2: a Fallback topic
For unexpected AI agent actions.
Scenario:
Unexpected AI agent actions must end in an escalation to a live representative. For example, a sales executive must be rerouted to a representative if the agent cannot answer a question after two failed attempts.
In a low-code environment using Microsoft Copilot Studio with Dataverse, you can ensure unexpected AI actions lead to a live representative by configuring specific system topics.
1. Configure the System Fallback Topic
The Fallback system topic triggers when the AI agent cannot match a user's intent to any existing topic or knowledge source with sufficient confidence.
Enable Fallback: In Copilot Studio, navigate to Settings > General Settings > System fallback and click Add.
Action: Edit the Fallback topic to include a Go to topic node that redirects to the Escalate system topic.
2. Implement the Escalate System Topic
The Escalate topic is the primary mechanism for handoffs.
3. Handle Errors and Infinite Loops
Reference:
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/low-code-plugins-copilot- studio
NEW QUESTION # 28
A company has an AI agent that automates the review of customer feedback stored in a cloud database.
You plan to generate monthly reports from the agent's output to provide insights into customer sentiment and guide product development and marketing.
You need to ensure that the data ingested by the agent is clean and suitable for the intended use.
What should you do to prepare the data?
Answer: D
Explanation:
To automate feedback assessment and monthly reporting, you can build an agent flow in Microsoft Copilot Studio that functions as a "data pre-processor" and "insight generator." By using Agent Flows, you can move beyond simple Q&A to create structured, multi-step processes that clean raw data before it is ingested by the agent's main knowledge base.
Step 1: Design the Feedback Agent Flow
The "Flow" acts as the bridge between your cloud database (e.g., Azure SQL, Cosmos DB) and the final report.
Step 2: Ensure Data Quality (The "Clean Room")
For the agent to give accurate insights for marketing, the data must be grounded and structured.
Step 3: Automate the Monthly Report
Once the data is clean, the agent can summarize it into a report suitable for stakeholders.
Reference:
https://cps.co.uk/insights/how-to-use-copilot-studio-to-automate-repetitive-tasks
NEW QUESTION # 29
Hotspot Question
You use Microsoft Copilot Studio analytics to analyze the performance of a deployed Copilot Studio agent.
You need to identify which performance metrics to use to measure the following:
- The percentage of engaged sessions that are escalated to a live
customer service representative
- The number of agent queries that cause a knowledge source error
What should you identify for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 30
A retail company plans to deploy Microsoft Copilot Studio agents to support:
- Microsoft Dynamics 365 Commerce scenarios.
- A Microsoft Power Apps inventory management solution.
You need to recommend a solution to organize product catalog data as a consistent source for multiple AI systems.
What should you recommend?
Answer: B
Explanation:
In the scenario described, centralizing product catalog data in Microsoft Dataverse is the recommended architectural approach to ensure consistency across multiple AI systems.
Dataverse acts as a unified "knowledge network" that allows different agents to share a single source of truth for both structured and unstructured data.
Key Benefits of Centralizing in Dataverse
Cross-App Consistency: By storing the catalog in Dataverse, both the Dynamics 365 Commerce agent and the Power Apps inventory management agent access the exact same records. This prevents data silos where inventory levels or product descriptions might drift apart between systems.
Native Copilot Studio Integration: You can directly add Dataverse tables as knowledge sources in Microsoft Copilot Studio. This allows agents to use Retrieval-Augmented Generation (RAG) to answer queries grounded in your live product data.
Security & Governance: Dataverse enforces Role-Based Access Control (RBAC), ensuring that agents only interact with data they are authorized to see, based on the user's existing permissions.
Automated Updates: You can use Power Platform Dataflows to ingest and synchronize catalog data from external sources into Dataverse, keeping the information fresh for all connected AI agents.
Reference:
https://www.microsoft.com/en-us/power-platform/blog/2025/06/16/data-agent-architecture- powered-by-microsoft-dataverse
NEW QUESTION # 31
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