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Microsoft AB-100 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Deploy AI-powered business solutions: Focuses on deploying, testing, monitoring, and optimizing AI solutions in production. It also includes managing ALM processes, performance monitoring, and ensuring security, governance, and responsible AI compliance.
Topic 2
  • Design AI-powered business solutions: Covers designing AI agents, Copilot integrations, and intelligent workflows using platforms like Copilot Studio, Microsoft Foundry, and Dynamics 365. It includes planning prompts, connectors, agent behaviors, and solution extensibility.
Topic 3
  • Plan AI-powered business solutions: Focuses on analyzing business requirements and identifying where AI agents and generative AI can improve processes. It also includes defining AI strategy, evaluating ROI, and deciding whether to build, buy, or extend AI components.

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Microsoft Agentic AI Business Solutions Architect Sample Questions (Q91-Q96):

NEW QUESTION # 91
Case Study 2 - Contoso, Ltd
Overview
Contoso, Ltd. is a high-tech manufacturing company that uses Microsoft Dynamics 365 Finance.
Dynamics 365 Supply Chain Management, and Dynamics 365 Commerce for its North American operations. The company designs and develops innovative products that have many patents and proprietary technologies. The patents and engineering designs are closely guarded secrets.
Contoso executives want to integrate and adopt AI solutions to help scale the company in preparation for an anticipated period of rapid growth.
The company has multiple legal entities and Azure subscriptions that will be used in the adopted AI solutions.
Requirements
AI Adoption
The following executives will have specific responsibilities in the overall AI adoption:
- Chief Technology Officer (CTO): Select one Dynamics 365 Finance,
Dynamics 365 Supply Chain Management or Dynamics 365 Commerce prebuilt
AI agent and one custom Microsoft Copilot Studio AI agent to prioritize and deploy during the initial AI adoption phase.
- Chief Information Officer (CIO): Ensure that appropriate security
labels are assigned to the data used by the AI agents.
- Chief Financial Officer (CFO): Analyze the return on investment (ROI) for the AI agents being deployed.
- Chief Information Security Officer (CISO): Discover and inventory AI
resources for auditing.
- Chief Executive Officer (CEO): Ensure that all solutions adhere to
industry-standard responsible AI practices.
All AI initiatives and agents will have a detailed business use case, a defined audience profile, and an estimated ROI that will compare the cost savings of the current process against the estimated costs of using the new AI solutions.
The company's research and development (R&D) department already has a custom Model Context Protocol (MCP) server that contains comprehensive product specifications and compliance data.
Prebuilt AI Agent
The CTO has NOT yet selected which prebuilt AI agent to use in Dynamics 365 Supply Chain Management. The CTO wants to view available agent templates to identify which agent will add the most business value.
Depending on which high-priority AI agents are identified, its agent capabilities must be previewed in a discovery meeting with the relevant business operation stakeholders.
Custom AI Agent
Contoso has identified the following custom AI agent requirements:
- The custom AI agent will use data from Dynamics 365 Supply Chain
Management to answer questions for the manufacturing team as a low-code solution.
- The custom AI agent will be accessible from within Microsoft Teams.
- The custom AI agent must be designed to eventually connect to other
agents that can be selected based on their description.
- The topics used in the custom AI agent will be selected based NOT on
a trigger phrase, but on a description of the purpose of the query, to
make the interactions more conversational.
- The custom AI agent must be able to answer questions about product
specifications by using existing technologies. The product
specifications are maintained by the R&D department.
- The custom AI agent must be integrated with and accessible from
Dynamics 365 Supply Chain Management.
- The custom AI agent must be able to use Dynamics 365 Supply Chain
Management business logic that is stored outside of the application.
Analysis, Reporting, and Troubleshooting
Contoso has identified the following analysis, reporting, and troubleshooting requirements:
- The CISO will audit all the AI solutions monthly for compliance and
security.
- The CFO will analyze all the AI solutions quarterly to compare the
estimated ROI against actual measured efficiencies and adoption. The
CFO will use the Copilot Studio agent usage estimator to perform this
analysis.
- The CISO wants to identify how much sensitive data was accessed for a given AI agent run and who accessed the data. Too much sensitive data accessed by a single user might indicate a high security risk.
- The CTO wants to track user feedback on the quality of the AI agent
responses during user interactions with the agents. Consistently poor
feedback will trigger an escalated reengineering discussion.
- The CEO wants a quarterly assessment of all the required metrics for
their specific responsibilities. The tools used for the assessments
must be Microsoft-recommended and must verify reliability,
interpretability, fairness, and compliance.
- The CFO wants to identify how many interactions with the AI agents
are abandoned on a given day as compared to resolved conversations. Too many abandoned sessions might indicate that Copilot Studio credits are being used inefficiently by end users.
Which two components in the custom AI agent design should the CFO evaluate in the quarterly agent analysis? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Answer: B,C

Explanation:
Scenario:
The CFO will analyze all the AI solutions quarterly to compare the estimated ROI against actual measured efficiencies and adoption. The CFO will use the Copilot Studio agent usage estimator to perform this analysis.
Quarterly Estimated ROI (Forecasting)
Use the Microsoft Agent Usage Estimator to model quarterly expectations before each period.
Orchestration Method Input: Select between Classic (logic-driven) or Generative (AI-driven) orchestration. Generative orchestration typically consumes more credits but reduces manual development time.
Session Time Variables: Model the average session time per agent to estimate total message volume. The estimator uses this to project credit consumption based on interaction depth.
Target ROI Formula: Define the benchmark as:
Estimated Savings = (Projected Deflection ร— Human Agent Cost) - Estimated Credit Cost.
Reference:
https://alrafayglobal.com/measure-your-ai-chatbot-roi-copilot-studio


NEW QUESTION # 92
You are designing a low-code Al business solution by using Microsoft Copilot Studio.
The solution must include an agent that automates tasks by simulating user interactions across third-party apps and websites, such as clicking buttons, entering text, and extracting information from screens.
You need to recommend what to include in the agent.
What should you recommend?

Answer: C

Explanation:
" Computer use is a tool in Copilot Studio that lets your agent interact with and automate tasks on a Windows computer. It works with websites and desktop apps by selecting buttons, choosing menus, and entering text into fields on the screen. Describe in natural language what you want computer use to do, and it performs the task on a computer you set up by using a virtual mouse and keyboard. By using computer use, agents can complete tasks even when there ' s no API to connect directly to the system. If a person can use an app or website, computer use can too. You can use computer use for tasks like automated data entry, invoice processing, and data extraction. " Reference: https://learn.microsoft.com/en-us/microsoft-copilot-studio
/computer-use


NEW QUESTION # 93
What should you include in the custom Al agent design to meet the R & D product specifications and the compliance information requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
To expose the data to the agent, create: a custom connector
Add to the agent: the MCP server
The custom agent must answer questions about product specifications and compliance information, and the case study states that the R & D department already has a custom Model Context Protocol (MCP) server containing that information.
The best design is:
create a custom connector to expose that external capability cleanly to the low-code Copilot solution add the MCP server to the agent so the agent can use that external knowledge/tooling source Why this is correct:
The MCP server already exists and contains the needed product-specification and compliance data.
In a Copilot/agent design, you need a way to expose external functionality and data in a reusable, secure way.
A custom connector is the low-code integration mechanism that fits this requirement.
Then the agent can use the MCP server as the connected external capability for answering those questions.
Why the other options are not correct:
Azure AI Bot Service channel is for communication channels, not for exposing this knowledge source.
a custom OData entity is not the right pattern for integrating the existing MCP-based capability.
the Semantic Kernel is a developer orchestration framework, but the requirement emphasizes using the existing MCP technology in a low-code solution.
an event trigger is unrelated to exposing R & D specification/compliance knowledge.
a REST API is too generic here; the scenario specifically points to the existing MCP server.
a tool is close conceptually, but the most direct answer choice tied to the existing R & D technology is the MCP server.


NEW QUESTION # 94
A company has a Microsoft Power Platform solution that contains the following components:
- Microsoft Dataverse tables
- A Microsoft Power BI workspace named WS1
- A canvas app named App1 that uses Dataverse
- A Power BI semantic model that connects to Dataverse by using
DirectQuery
You plan to use generative AI to provide answers to queries based on a subset of corporate data.
You need to ensure that the data is available as a grounding data source for AI systems.
What should you do?

Answer: C

Explanation:
Consider using tags to label semantic models as ready for Copilot consumption. This method helps data consumers identify models that they can use with Copilot and expect better results.
Alternatively, consider the readiness of a semantic model for use with Copilot as a criteria for its endorsement to promoted or even certified status.
Reference:
https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-semantic-models


NEW QUESTION # 95
A company plans to implement an AI solution that will contain a Microsoft Copilot Studio agent and a Microsoft Foundry agent. The solution will be stored in a source code repository.
You need to recommend a deployment method for each agent. The solution must meet the following requirements:
A test environment must be used before a deployment to production.
Production must be isolated from development and testing.
The deployment must be repeatable and fully automated.
The solution must NOT require manual intervention.
Which deployment method should you recommend for each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Copilot Studio # Use a Microsoft Power Platform deployment pipeline; Microsoft Foundry # Use an Azure DevOps pipeline


NEW QUESTION # 96
......

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