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| Section | Weight | Objectives |
|---|---|---|
| Plan AI-powered business solutions | 25–30% | - Design overall AI strategy
|
| Deploy AI-powered business solutions | 40–45% | - Monitor, analyze, and tune solutions
|
| Design AI-powered business solutions | 25–30% | - Design AI and agents
|
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NEW QUESTION # 102
You need to design a Microsoft Copilot Studio agent for customer support. The agent must securely retrieve product warranty data from a REST API. The solution must minimize development effort. What should you include in the design?
Answer: D
Explanation:
The requirement is to build a Microsoft Copilot Studio agent that can securely retrieve product warranty data from a REST API while minimizing development effort.
The best choice is D. Create a custom connector in Copilot Studio and use the connector to call the API.
Why this is correct:
A custom connector is the standard low-code way to integrate Copilot Studio with a REST API. It lets the agent call the external warranty service securely, using supported authentication methods, without requiring heavy custom development. This directly satisfies both key requirements:
secure retrieval of warranty data
minimal development effort
NEW QUESTION # 103
A company has a Microsoft Copilot Studio agent that uses custom connectors to interact with enterprise APIs.
You need to recommend an application lifecycle management (ALM) process to ensure that the connectors are deployed consistently across development, test, and production environments and meet governance and traceability requirements.
What should you recommend?
Answer: C
NEW QUESTION # 104
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
A key stakeholder in your organization is championing the immediate deployment of Microsoft Copilot across all business units. Their primary justification is the belief that AI will automatically generate superior, data-driven decisions starting on day one, irrespective of the current state of organizational data quality or the alignment of existing business processes. You are tasked with providing an accurate assessment of this claim based on industry guidance, particularly Microsoft's "AI for Business" principles.
Based on Microsoft's "AI for Business" guidance, can you confidently state that deploying AI tools like Copilot will lead to better decisions with having a foundation of quality business data and well- defined, aligned workflows first?
Answer: A
Explanation:
While Microsoft's "AI for Business" guidance indeed emphasizes that AI can enable better decision-making, it critically states that this capability is not automatic or instantaneous. Instead, it explicitly requires several foundational elements:
Processing large volumes of relevant, quality business data: AI tools like Copilot depend heavily on access to accurate, comprehensive, and well-structured organizational data to derive meaningful insights. Without quality data, the AI lacks the necessary inputs to provide intelligent, contextual, or reliable outputs.
Uncovering patterns: AI's strength lies in identifying patterns and correlations within data that human analysis might miss. This process is futile if the underlying data is incomplete, inaccurate, or unstructured.
Aligning the technology to business workflows: For AI-generated insights to translate into genuinely "better decisions," they must be integrated into and relevant to existing business processes. Deploying AI in a vacuum, without considering how it fits into and enhances current workflows, will diminish its practical value and impact on decision-making.
Therefore, deploying AI tools like Copilot without an existing foundation of quality data and aligned processes will not automatically guarantee improved decisions. The AI will lack the essential context and reliable inputs needed to generate meaningful insights and drive effective outcomes.
References:
https://www.microsoft.com/en-in/microsoft-copilot/copilot-101/ai-for-
business#Customerexperience
https://www.microsoft.com/en-in/microsoft-365/business-insights-ideas/resources/grow-your- small-business-with-artificial-intelligence
NEW QUESTION # 105
A startup wants to build a customizable, agent-based workflow that can integrate with their internal APIs, retrieve contextual data from various sources, and run complex business logic autonomously. The team has moderate engineering skills but explicitly wants to avoid the overhead of managing underlying infrastructure. Which Microsoft AI service model best fits this requirement for building and deploying their agent-based solution?
Answer: A
Explanation:
PaaS (Microsoft Foundry + Azure OpenAI + Azure AI Search + Prompt Flow) is correct because Platform-as-a-Service models, particularly those leveraging Azure AI services, provide the necessary tools and flexibility for building custom AI agents and orchestration. Microsoft Foundry (formerly Azure AI Foundry) offers a comprehensive environment, Azure OpenAI provides the LLM power, Azure AI Search enables contextual data retrieval, and Prompt Flow helps with orchestration and evaluation. Crucially, PaaS abstracts away infrastructure management, aligning with the startup's requirement.
References:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/prompt-flow
https://www.microsoft.com/en-us/ai
NEW QUESTION # 106
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 existing tool and data should you use to gather the required metrics for stakeholder signoff for the AI agents? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Scenario:
The return on investment (ROI) of switching from the current process to the future process is required for stakeholder sign off.
Box 1: Microsoft Foundry
Tool
To secure stakeholder sign-off for a Microsoft AI solution using AI agents and Dataverse, Azure AI Foundry (formerly Azure AI Studio) provides a structured framework to gather metrics across five key "ROI levers":
1. Identify Key ROI Metrics via Foundry
Use the Foundry ROI Framework to categorize and collect data that compares the current manual process to the future AI-driven state.
2. Gather Data using Foundry Tools
Foundry offers specific technical features to generate the evidence needed for your business case.
3. Calculate the ROI
Once metrics are gathered, apply the standard Microsoft-recommended formula for the business case.
Box 2: the current time to complete the task today per instance
Data required for the tool
In the Microsoft Foundry ROI framework, the metric for current time required to complete a task today is typically referred to as the Current Time per Instance or Task Duration (Baseline). This value serves as the primary "Before" benchmark to calculate the efficiency gains of the new AI agent solution.
Reference:
https://itnext.io/stop-wasting-tokens-how-to-design-high-roi-ai-apps-on-microsoft-foundry-616622ddead6
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/a-framework-for-calculating-roi-for-agentic-ai-apps/4369169
NEW QUESTION # 107
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