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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Exam Duration: | 100 minutes |
| Exam Format: | Interactive question types, Case studies, Scenario-based questions, Multiple choice |
| Certificate Validity Period: | 12 months |
| Available Languages: | English |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals Microsoft Certified: Dynamics 365 Microsoft Certified: Power Platform |
| Recommended Training: | Microsoft Copilot Studio Learning Path Azure AI Foundry Documentation |
| Exam Registration: | Microsoft Learn Certification Page AB-100 Exam Study Guide |
| Sample Questions: | Microsoft AB-100 Sample Questions |
| Exam Way: | Online proctored exam (with possible interactive/case-based components) |
| Pre Condition: | Recommended: Active Microsoft Associate-level certification in AI/Power Platform/Dynamics 365 (e.g., AI-102, PL-600, MB-910 or similar) |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/agentic-ai-business-solutions-architect/ |
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NEW QUESTION # 92
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,D
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 # 93
A company has a Microsoft 365 tenant in Canada and multiple Microsoft Power Platform environments in Canada and the United States. The company plans to deploy a Microsoft Copilot Studio agent to the Canadian environment that will use:
* Microsoft Dataverse data stored in Canada
* A connector that connects to an Azure OpenAI instance in the United States You need to ensure that the agent adheres to data residency and data movement policies before being deployed. What should you do?
Answer: C
Explanation:
The key issue is that the agent will run in a Canadian environment and use:
* Dataverse data stored in Canada
* a connector to Azure OpenAI in the United States
That means data may need to move across regions . Before deployment, the organization must make sure this cross-region use is explicitly allowed under the platform's data movement and residency controls.
That makes C the correct answer.
Why C is correct:
* It directly addresses the fact that the solution uses services in different geographic regions
* It ensures the environment and its connector dependencies are configured to allow that movement in line with platform policy
* It is the most specific action tied to data residency and data movement compliance before deployment Why the other options are not correct:
* A. Ensure that the data processed by Azure OpenAI is stored in the United States. This does not address whether the cross-region movement itself is permitted.
* B. From the Microsoft Purview portal, validate the Data loss prevention settings. DLP helps govern connector usage and data exfiltration, but the question is specifically about data residency and cross-region data movement .
* D. Migrate the tenant to the United States. This is unnecessary and does not align with the stated Canadian deployment requirement.
NEW QUESTION # 94
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 company uses Microsoft 365 and Dynamics 365.
You need to recommend a solution to automatically summarize email threads, generate suggested replies in Microsoft Outlook, and provide meeting preparation summaries that include relevant customer relationship management (CRM) data.
Solution: You recommend Microsoft 365 Copilot for Sales.
Does this meet the goal?
Answer: A
Explanation:
Correct:
* You recommend Microsoft 365 Copilot for Sales.
Incorrect:
* You recommend a classic Microsoft Dataverse workflow.
* You recommend a Microsoft 365 Copilot agent template.
Note:
In the described scenario, Microsoft 365 Copilot for Sales acts as the primary bridge between your productivity tools and CRM data. It integrates directly into Microsoft Outlook and Teams to surface real-time insights from Dynamics 365 Sales or Salesforce.
Key capabilities for this specific workflow include:
Automated Email Summarization: Copilot scans long email threads in Outlook to extract key points, highlights, and BANT (Budget, Authority, Need, Timeline) data. If the sender is an external contact recognized in your CRM, the summary is automatically enriched with relevant account and opportunity data.
Suggested Email Replies: When replying to customer emails, Copilot generates drafts based on the context of the conversation and existing CRM data. You can use predefined response categories (e.g., "Reply to an inquiry," "Offer a proposal") or custom prompts to include specific opportunity details in the draft.
Meeting Preparation Summaries: Before a scheduled meeting, Copilot for Sales provides a
"preparation card" in Teams or Outlook. This summary includes:
- CRM Data: Matched opportunity and account attributes.
- Contextual History: Summaries of past email exchanges and the last three seller notes.
- Strategic Insights: Key risks, follow-up actions, and discussion points from previous interactions.
Reference:
https://msdynamicsworld.com/blog/microsoft-copilot-sales-close-deals-faster-ai
NEW QUESTION # 95
A company uses Azure OpenAI models that use grounding data from Microsoft Fabric for agents. The models are fine-tuned by using proprietary datasets.
You need to design a governance solution that meets the following requirements:
Restricts access to the grounding data to only assigned roles
Restricts model fine-tuning to only the AI engineering team
What should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Restricts access to grounding data # Microsoft Purview access policies; Restricts model fine-tuning # Role- based access control (RBAC) in Microsoft Foundry Why Microsoft Purview access policies is correct The grounding data is stored in Microsoft Fabric , and the requirement is to restrict access to that data to only assigned roles.
That is a data governance and access control requirement. Microsoft Purview access policies are the best fit because they are designed to govern and control access to data across enterprise data estates. In this case, they help ensure that only authorized roles can access the grounding data used by the agents.
From an AI business solutions perspective, grounding data is often one of the most sensitive parts of the solution because it can contain:
* proprietary business knowledge
* internal documents
* regulated operational information
* contextual data used to shape model outputs
Purview helps enforce governed access to that data layer rather than relying only on general infrastructure controls.
Why RBAC in Microsoft Foundry is correct
The second requirement is to ensure that only the AI engineering team can perform model fine-tuning .
That is an action-level platform permission requirement. The best control for that is role-based access control (RBAC) in Microsoft Foundry .
RBAC allows the organization to assign permissions based on job function, so only authorized users or groups can:
* create or modify fine-tuning jobs
* manage model assets
* update training configurations
* control deployment-related AI resources
This is the right governance pattern because fine-tuning changes model behavior and can introduce:
* security risk
* compliance risk
* quality drift
* misuse of proprietary datasets
Restricting that capability to the AI engineering team through RBAC creates a clear separation of duties.
Why the other options are incorrect
Azure AI Content Safety
This is used to detect and filter harmful content. It does not control access to Fabric grounding data.
Azure Monitor alerts
Alerts help observe activity, but they do not enforce role-based access to data.
Azure Policy compliance rules
Azure Policy is useful for enforcing resource configuration standards, but it is not the best answer for role- based access to Fabric grounding data or for limiting fine-tuning actions to a specific team.
Azure Resource Manager (ARM) resource locks
Resource locks help prevent deletion or modification of Azure resources, but they do not provide the right permission model for controlling who can perform model fine-tuning operations.
Microsoft Entra Conditional Access
Conditional Access is mainly about sign-in and access conditions, such as device, location, or risk context. It is not the best direct control for restricting fine-tuning permissions inside Foundry.
Expert reasoning
Use this exam shortcut:
* Need to control access to enterprise data # think Purview access policies
* Need to restrict who can perform AI platform actions like fine-tuning # think RBAC in the AI platform So the correct mapping is:
* Restricts access to the grounding data: Microsoft Purview access policies
* Restricts model fine-tuning: Role-based access control (RBAC) in Microsoft Foundry
NEW QUESTION # 96
A company uses a fine-tuned Microsoft Foundry model that requires frequent updates as new customer feedback becomes available.
You need to design an application lifecycle management (ALM) process that meets the following requirements:
Data changes must be tracked and versioned.
The model must be retrained consistently by using approved training data.
Which two actions should you include in the design?
NOTE: Each correct selection is worth one point.
Answer: A,E
NEW QUESTION # 97
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