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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Passing Score: | 700 (out of 1000) |
| Real Exam Qty: | Around 40-50 questions |
| Certificate Validity Period: | 1 year (for recertification) |
| Exam Price: | USD $85 |
| Exam Duration: | 50 minutes |
| Related Certifications: | Microsoft Applied Skills |
| Exam Format: | Scenario-based, Multiple choice |
| Available Languages: | English |
| Sample Questions: | Microsoft AB-100 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | Basic understanding of Microsoft Copilot, Azure AI services, and general AI/ML concepts recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/applied-skills/agentic-ai-business-solutions-architect/ |
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NEW QUESTION # 22
A company has a Microsoft Foundry agent that summarizes customer feedback and recommends products to customers. The agent references data from multiple knowledge sources.
Users report that the agent response time is slow.
Telemetry data shows that the agent frequently reaches its token usage limit You need to recommend a solution to reduce token usage without degrading the quality of the generated responses.
What should you recommend?
Answer: D
Explanation:
The problem is not just that the agent is slow. The telemetry specifically says it frequently reaches its token usage limit. That means too much content is being pulled into the prompt or context window before the model generates the answer.
The best recommendation is D. Reconfigure the prompts to limit the amount of retrieved content from the knowledge sources.
Why D is correct:
It directly targets the root cause: too many tokens from retrieved context It reduces unnecessary context while still keeping the most relevant information It helps preserve response quality better than simply cutting capabilities or hard-limiting output size
NEW QUESTION # 23
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: C
Explanation:
Correct:
* Identify and address biased data.
To ensure that the customer feedback ingested by your Microsoft AI agent is clean and suitable for monthly sentiment reports, the most effective action is to identify and address biased data.Data bias in sentiment analysis can lead to skewed reports that misrepresent customer satisfaction or ignore the needs of specific demographic groups.
Incorrect:
* Create a workflow in Microsoft Power Automate
* Create an agent flow in Microsoft Copilot Studio.
* Ensure that the size of the database does not exceed 100 GB.
* Sort the database by customer last name.
Reference:
https://blog.naitive.cloud/how-to-detect-and-mitigate-bias-in-generative-ai/
NEW QUESTION # 24
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: A
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 # 25
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.
Which tool should you recommend to address the sensitive information concerns in the sales process?
Answer: A
Explanation:
Scenario:
Fabrikam has identified the following requirements for sales cycle enablement:
Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
In a Microsoft AI solution involving an agent where you must track sensitive information for auditing, you should use the Microsoft Foundry Tracing UI.
Tracking and Auditing with Microsoft Foundry Tracing
The Microsoft Foundry Tracing UI (part of Microsoft Foundry) provides end-to-end visibility into agent runs by capturing detailed telemetry through OpenTelemetry.
What is Captured: It automatically logs user inputs, model outputs, and tool arguments, which often contain sensitive information like names or IDs.
Auditing and Debugging: You can search, filter, and sort traces from the last 90 days to observe exactly how your agent responded during specific sessions.
Infrastructure: Traces are stored in Azure Application Insights, allowing you to apply standard enterprise access controls and retention policies.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/observability/concepts/trace-agent-concept
NEW QUESTION # 26
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 for the custom AI agent should you include in the application lifecycle management (AIM) process? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
[C]
A Microsoft Power Platform solution is the standard container for transporting and managing customizations across the Power Suite (Copilot Studio, Power Automate, and Power Apps). It perfectly aligns with the specific requirements.
[B]
Based on the application lifecycle management (ALM) process for a Microsoft Power Platform solution that integrates with Dynamics 365 Supply Chain Management, you should use a ZIP package.
Recommended ALM Component
In the Microsoft Power Platform ecosystem, the standard unit of deployment for ALM is a Solution, which is exported and transported as a ZIP package.
ZIP package: This is the standard format for Power Platform solutions containing Copilot Studio agents, Power Automate flows, and custom connectors used to bridge Dynamics 365 and Teams.
Incorrect:
[Not A]
Azure package: Typically refers to Azure-native resources; while used for custom coding, the Power Platform solution itself is handled via ZIP exports.
[Not D]
Cloud Scale Unit (CSU) package: Specifically for commerce or edge workloads, not for deploying general AI agents.
[Not E]
X++ model is used for core code changes inside Dynamics 365 Finance & Operations, but not for the low-code Power Platform/Copilot Studio orchestration described.
Reference:
https://thorprojects.com/2025/09/23/copilot-studio-power-on-the-power-platform/#
https://learn.microsoft.com/en-us/dynamics365/supply-chain/procurement/supplier-com-agent-setup
NEW QUESTION # 27
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