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Microsoft AB-100 Exam Syllabus Topics:

SectionWeightObjectives
Design AI-powered business solutions25–30%- Design extensibility and integration
  • 1. Integrate with Microsoft 365, Teams, and SharePoint
  • 2. Extend agents via Model Context Protocol and open standards
  • 3. Apply Power Platform Well-Architected Framework
- Orchestrate configuration
  • 1. Configure AI features across Dynamics 365 workloads
  • 2. Design knowledge source integration
  • 3. Set up Microsoft 365 Copilot for Sales and Service
- Design AI and agents
  • 1. Design topics, flows, and actions in Copilot Studio
  • 2. Customize Copilot for Dynamics 365 applications
  • 3. Integrate Azure AI services and OpenAI models
  • 4. Build task, autonomous, and prompt-response agents
Plan AI-powered business solutions25–30%- Evaluate costs and benefits
  • 1. Define ROI and total cost of ownership criteria
  • 2. Perform ROI analysis for proposed solutions
  • 3. Design model routing strategies
- Design overall AI strategy
  • 1. Design multi-agent solutions using Microsoft 365, Copilot Studio, Azure AI Foundry
  • 2. Establish responsible AI and governance guidelines
  • 3. Determine build vs buy vs extend decisions
  • 4. Apply Cloud Adoption Framework for AI
  • 5. Define architecture strategy for AI and agents
- Analyze requirements for AI-powered business solutions
  • 1. Evaluate data quality, relevance, and availability for grounding
  • 2. Assess agent use in automation, analytics, and decision-making
  • 3. Organize data for reuse across AI systems
Deploy AI-powered business solutions40–45%- Monitor, analyze, and tune solutions
  • 1. Optimize agent behavior and reliability
  • 2. Design monitoring and telemetry strategies
  • 3. Analyze usage, feedback, and performance metrics
- Manage testing and validation
  • 1. Define test criteria for agents and models
  • 2. Validate prompt engineering best practices
  • 3. Design end-to-end test scenarios
- Implement security, governance, compliance
  • 1. Adhere to responsible AI principles
  • 2. Secure models, data, and workflows
  • 3. Audit trails and vulnerability management
  • 4. Enforce data residency and access controls
- Design ALM processes
  • 1. Version control and release management
  • 2. ALM for agents, models, data, and configurations

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Microsoft Agentic AI Business Solutions Architect Sample Questions (Q27-Q32):

NEW QUESTION # 27
Hotspot Question
A company uses Microsoft Dynamics 365 Supply Chain Management.
You are designing an AI supply chain process that meets the following requirements:
- Provides managers with AI-driven insights that surface key
information from customer orders
- Helps planners use AI to anticipate future product needs more
accurately
You need to recommend which Microsoft Copilot features to include in the design.
What should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: AI summaries with Copilot
Provides managers with AI-driven insights that surface key information from customer orders To provide managers with AI-driven insights from customer orders in Dynamics 365 Supply Chain Management, use the AI summaries with Copilot feature, specifically leveraging the embedded Customer Summary capabilities. This tool provides instant, personalized overviews of critical data, such as backordered items, credit limits, and risk indicators, directly within the workspace.
Box 2: Generative insights for Demand planning
Helps planners use AI to anticipate future product needs more accurately The Copilot Generative insights for Demand planning feature in Microsoft Dynamics 365 Supply Chain Management enables demand planners to move from reactive, manual forecasting to proactive, AI-driven demand anticipation. By analyzing, clustering, and interpreting vast datasets, Copilot helps identify patterns such as seasonality and signal correlations to improve accuracy.
Reference:
https://www.randgroup.com/insights/tip-of-the-month/finance-operations-totm/dynamics-365- finance-operations-tip-of-the-month-work-smarter-with-copilot-in-d365-finance-supply-chain- management/
https://learn.microsoft.com/en-us/dynamics365/release-plan/2025wave2/enterprise-resource- planning/dynamics365-supply-chain-management/analyze-demand-enhanced-generative- insights-demand-planning


NEW QUESTION # 28
A company is designing a Microsoft Power Platform solution to reduce the manual steps of a business process by deploying an existing AI model.
You need to calculate the return on AI investment (ROAI) by identifying the metadata and telemetry of the solution.
What should you use?

Answer: C

Explanation:
The Business Value Toolkit is the correct resource to use for calculating Return on AI Investment (ROAI) for a Microsoft Power Platform solution.
Calculating ROAI with the Business Value Toolkit
The Business Value Toolkit helps organizations move beyond simple automation by providing structured templates and analytics to justify AI investments.
Metadata Identification: It captures environment-specific data, such as the number of automated flows, the type of AI models used (e.g., AI Builder), and the business units involved.
Telemetry Integration: It leverages usage data from the Power Platform admin center and Application Insights to track real-time performance, such as execution frequency and success rates.
ROI Metrics: It transforms technical telemetry into financial outcomes, such as:
- Time saved: Hours recovered from manual data entry or processing.
- Error reduction: Improvements in accuracy compared to manual steps.
- Cost avoidance: Savings from reduced reliance on specialized manual labor.
Incorrect:
[Not D]
While the Cloud Adoption Framework (CAF) for Azure provides broad strategic guidance for AI adoption, the Business Value Toolkit is specifically designed to quantify the impact of low-code and AI solutions using solution metadata and telemetry.
Reference:
https://learn.microsoft.com/en-us/power-platform/guidance/coe/business-value-toolkit


NEW QUESTION # 29
A company has a Microsoft Power Platform solution that contains the following components:
* Microsoft Dataverse tables
* A Microsoft Power Bl workspace named WS1
* A canvas app named App1 that uses Dataverse
* A Power Bl semantic model that connects to Dataverse by using DirectQuery You plan to use generative Al 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 Al systems. What should you do?

Answer: B

Explanation:
The goal is to use generative AI to answer questions based on a subset of corporate data, and to ensure that this data is available as a grounding data source.
The solution includes:
Dataverse tables
a Power BI workspace
a canvas app
a Power BI semantic model using DirectQuery to Dataverse
The best action is D. Endorse the semantic model.
Why D is correct:
Endorsing a semantic model makes it a trusted, discoverable enterprise data asset It is the appropriate step when you want approved corporate data to be used reliably by downstream AI and analytics experiences It fits the requirement of exposing a curated subset of data as a grounding source rather than duplicating or manually exporting it Why the other options are not correct:
A). Export the semantic modelExporting does not make it a governed grounding source.
B). Share WS1Sharing the workspace grants access, but it does not establish the semantic model itself as the trusted data source for grounding.
C). Populate a Dataverse tableThe data already exists and is modeled through the semantic layer; creating another table is unnecessary for this requirement.


NEW QUESTION # 30
Scenario: Your customer needs their custom AI agent to interact seamlessly and securely with multiple internal enterprise systems, including their ERP, CRM, and various legacy order processing APIs. They are looking for a standardized, future-proof method for this integration that minimizes the need for developing and maintaining bespoke, fragile custom connectors for every single endpoint.
Based on Microsoft's recommended guidance for agent interoperability, which integration approach should you implement to achieve this standardized and robust cross-system communication?

Answer: C

Explanation:
Implement the Model Context Protocol (MCP) by exposing the backend REST APIs as MCP servers is correct because the Model Context Protocol (MCP) is Microsoft's strategic initiative designed to provide a standardized, universal protocol for AI agents to communicate with tools, services, and other agents. By exposing existing REST APIs as MCP servers, the customer can achieve standardized, robust, and future-proof interoperability for their AI agent across diverse internal systems, avoiding the pitfalls of custom, one-off connectors.
References:
https://learn.microsoft.com/en-us/azure/api-management/export-rest-mcp-server
https://learn.microsoft.com/en-us/microsoft-copilot-studio/agent-extend-action-mcp


NEW QUESTION # 31
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: B

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 # 32
......

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