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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Agentic AI Business Solutions Architect |
| Exam Number: | AB-100 |
| Certificate Validity Period: | 12 months |
| Exam Format: | Case studies, Scenario-based questions, Interactive question types, Multiple choice |
| Available Languages: | English |
| Exam Duration: | 100 minutes |
| Related Certifications: | Microsoft Certified: Power Platform Microsoft Certified: Azure AI Fundamentals Microsoft Certified: Dynamics 365 |
| Recommended Training: | Azure AI Foundry Documentation Microsoft Copilot Studio Learning Path |
| Exam Registration: | AB-100 Exam Study Guide Microsoft Learn Certification Page |
| 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 # 43
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: A
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 model Exporting does not make it a governed grounding source.
* B. Share WS1 Sharing the workspace grants access, but it does not establish the semantic model itself as the trusted data source for grounding.
* C. Populate a Dataverse table The data already exists and is modeled through the semantic layer; creating another table is unnecessary for this requirement.
NEW QUESTION # 44
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 # 45
A company has an AI business solution.
You need to extend the solution so that Microsoft 365 Copilot can invoke external logic hosted in Azure services.
What should you include in the solution?
Answer: A
Explanation:
To enhance an AI business solution with Microsoft 365 Copilot and integrate external logic hosted in Azure, you should use Copilot Studio to create Actions. These actions act as plugins that allow Copilot to invoke external services through Power Platform components.
Implementation Strategy
Azure Logic Hosting: Host your external logic in Azure using services like Azure Functions or Azure Logic Apps. These provide the API endpoints that Copilot will ultimately call.
*-> Power Platform Connector: Create a Custom Connector in the Power Platform to wrap your Azure service's API. This connector acts as the bridge, translating Copilot's requests into API calls your Azure logic understands.
Copilot Studio Integration: Within Microsoft Copilot Studio, add the custom connector as a Tool or Action. This makes the logic discoverable and invokable by Microsoft 365 Copilot.
Deployment: Deploy the action through the Microsoft 365 admin center under Integrated Apps to make it available to users in Teams or other Microsoft 365 apps.
Key Components
*-> Connector: Wraps the Azure API using an OpenAPI definition or Postman collection.
Plugin/Action: Defines how Copilot identifies when to use the connector based on user prompts.
Authentication: Ensure the connector is configured with appropriate security (e.g., OAuth 2.0) to safely access your Azure resources.
Reference:
https://learn.microsoft.com/en-us/copilot/security/connector-logicapp
NEW QUESTION # 46
A company uses Microsoft Foundry agents.
You need to ensure that an agent can dynamically use external tools at runtime without updating the agent.
What should you include in the solution?
Answer: A
Explanation:
The correct answer is A. a Model Context Protocol (MCP) server.
In Microsoft Foundry agent scenarios, MCP is used to let an agent discover and use external tools dynamically at runtime without requiring the agent itself to be modified each time a new tool or capability is added.
Why this is correct:
MCP standardizes how tools are exposed to agents
It allows the agent to connect to external capabilities in a flexible, runtime-driven way New tools can be made available through the MCP server without rebuilding or updating the core agent definition This directly matches the requirement:
"ensure that an agent can dynamically use external tools at runtime without updating the agent." Why the other options are not correct:
B). a Microsoft Foundry hubA hub is used more for organizing and managing AI resources/projects, not specifically for dynamic runtime tool exposure.
C). Microsoft Copilot StudioCopilot Studio is for building conversational agents and workflows, but the question is specifically about dynamic external tool use in Foundry agents.
D). Azure AI SearchAzure AI Search is for indexing and retrieving knowledge, not for dynamically exposing executable external tools.
NEW QUESTION # 47
Hotspot Question
A company deploys agents that generate responses by using Azure OpenAI resources. The agents are deployed to both the United States and Europe.
You need to recommend a governance solution that meets the following requirements:
- Enforces the deployment of the resources to only approved Azure
regions
- Provides continuous compliance verification of the resources
What should you include in the recommendation 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: Azure Policy
Enforces the deployment of the resources to only approved Azure regions To enforce the deployment of Azure OpenAI resources to only approved Azure regions (e.g., specific regions in Europe and the USA), you should use Azure Policy with the "Allowed locations" policy definition.
Here is the breakdown of how to implement this control:
Primary Tool: Azure Policy
Azure Policy allows you to define rules that restrict where resources can be created.
Policy Rule: Use the Allowed locations policy definition.
Implementation: Assign this policy at the Subscription or Resource Group level to restrict developers to only using permitted regions (e.g., East US, West Europe).
Effect: If a user attempts to deploy an Azure OpenAI resource in a non-approved region, the deployment will be blocked.
Box 2: Microsoft Purview
Provides continuous compliance verification of the resources
To provide continuous compliance verification for Azure OpenAI resources across Europe and the USA, you should use Microsoft Purview Compliance Manager and Azure Policy.
Microsoft Purview Compliance Manager: This tool provides a risk-based compliance score and continuous monitoring against global regulations such as the EU AI Act, GDPR, and various US standards. It offers specific regulatory templates to help you assess and implement controls for generative AI applications.
Azure Policy: Use this to enforce organizational standards and assess compliance at scale. You can apply built-in policy definitions for Azure AI services to automatically audit or deny non- compliant resource configurations, such as ensuring resources are restricted to specific regions (e.g., only EU or USA) or have private network access enabled.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/deployment-types
https://learn.microsoft.com/en-us/purview/ai-agent-365
NEW QUESTION # 48
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