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Microsoft AB-100 Exam Questions
Agentic AI Business Solutions Architect (Page 3 )

Updated On: 12-Apr-2026
Page 2 of 8
PDF with 56 Questions
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HOTSPOT

Which framework should you use to meet the AI agent requirements for the sales cycle enablement? To answer, select the appropriate options in the answer area.

Note: Each correct selection is worth one point.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: the ALM Accelerator for Microsoft Power Platform
For Microsoft Copilot Studio best practices

Using the ALM Accelerator for Microsoft Power Platform is a recommended approach for managing the lifecycle of a low-code AI agent (Copilot Studio) that relies on Dataverse. It enables source control, versioning, and automated deployment of AI agents to ensure they follow Microsoft's best practices.

Box 2: Microsoft Power Platform Well-Architected framework For conversational user experience

Utilizing the Microsoft Power Platform Well-Architected framework for a low-code AI agent (built in Copilot Studio) with Dataverse as the core data component ensures the solution is secure, reliable, and provides a high-quality conversational user experience (CUX). The framework helps align the agent with Microsoft's best practices for responsible AI, efficiency, and user satisfaction.

Scenario:
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.

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.


Reference:

https://learn.microsoft.com/en-us/power-platform/guidance/alm-accelerator/overview https://learn.microsoft.com/en-us/training/modules/adopt-ai-agent-best-practice



View Related Case Study

Which framework should you use for the infrastructure migration?

  1. Microsoft Cloud Adoption Framework for Azure
  2. Success by Design
  3. Microsoft Power Platform Center of Excellence (CoE)
  4. Microsoft Power Platform Project Setup Wizard

Answer(s): A

Explanation:

For migrating a legacy on-premises infrastructure to Microsoft Dynamics 365 Sales with Dataverse as the Single Source of Truth (SSOT), the recommended framework is the Microsoft Cloud Adoption Framework for Azure (CAF), specifically utilized in conjunction with the Data Management Framework (DMF) for Dynamics 365.
This combined approach ensures a structured transition by focusing on both the strategic adoption of cloud technology and the technical, granular migration of data.
Recommended Framework: Microsoft Cloud Adoption Framework (CAF) The CAF provides a holistic structure to ensure the migration is secure, compliant, and aligned with business goals.
Plan: Assess legacy data, prioritize workloads, and define the SSOT requirements.
Ready: Set up the Dataverse environment (landing zone) and configure security (Azure Active Directory/ Microsoft Entra ID).
Adopt (Migrate): Perform the technical migration of data using ETL (Extract, Transform, Load) processes.
Scenario:
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.

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.


Reference:

https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/overview



A company uses Microsoft Dynamics 365 Sales to manage leads that are stored in a Microsoft Dataverse table named Lead and use non-standard terminology and custom columns.

You need to configure business terms in the Lead table so that Microsoft Copilot controls can summarize the leads efficiently. The solution must minimize administrative effort.

How should you configure the business terms?

  1. Combine all the fields into one custom field.
  2. Map the field display names as business terms.
  3. Add the schema names as business terms.
  4. Create new business terms for each field.

Answer(s): B

Explanation:

To configure Microsoft Copilot to efficiently summarize leads with non-standard terminology and custom columns in Microsoft Dynamics 365 Sales, you must map these unique fields to business terms within the Sales AI Glossary in Microsoft Copilot Studio.
Note:
To map your field display names as business terms:
1. Access Copilot Studio: Open Microsoft Copilot Studio and select the environment containing your Dynamics 365 Sales instance.
2. Select the Sales Agent: Navigate to Agents and select the agent named Copilot in Dynamics 365 Sales (formerly Sales Copilot Power Virtual Agents Bot).
3. Navigate to Knowledge: Under the Knowledge section, select the SalesSpecificQnA knowledge source.
4. Add Glossary Entries:
Go to the Glossary tab.
Term: Enter the non-standard or custom field display name (e.g., your custom business term).
Description: Define how this term relates to the Dataverse schema. This helps Copilot understand the logic behind the custom column.
5. Configure Synonyms: In the Synonyms section, map your custom field to alternative names that sellers might use in natural language queries (e.g., mapping "Custom Revenue" to "Opportunity Revenue").
6. Publish Changes: Select Publish to apply these mappings, allowing Copilot to use the newly defined terms when generating lead summaries.


Reference:

https://learn.microsoft.com/en-us/dynamics365/sales/extend-copilot-chat



DRAG DROP

You are designing two Microsoft Copilot Studio agents named Agent1 and Agent2. Each agent must meet the following requirements:

Each agent must use a standard model.

Each agent must NOT use generative orchestration.

Agent1 must support simple and short phrases for a given topic.

Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel.

You need to recommend language models for the agents.

What should you recommend for each agent? To answer, drag the appropriate language models to the correct agents. Each language model may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Note: Each correct selection is worth one point.

Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Natural Language Understanding (NLU)
Agent1 must support simple and short phrases for a given topic.

For a Microsoft Copilot Studio agent that must not use generative orchestration and requires support for simple, short trigger phrases, the best choice is the Classic NLU (Natural Language Understanding) model.

When you disable generative orchestration (also known as "Generative mode" or "Generative AI" orchestration), the agent reverts to Classic orchestration. In this mode, the agent relies on predefined trigger phrases to map user input directly to specific topics.

Box 2: Natural Language Understanding + (NLU +)
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel.

For a Microsoft Copilot Studio agent using classic orchestration (no generative orchestration) and integrating with the Dynamics 365 Contact Center voice channel, the best language model is NLU+.

Why NLU+ is the Best Choice

While standard agents offer three "classic" Natural Language Understanding (NLU) options, NLU+ is specifically designed for high-performance, enterprise-grade scenarios like voice channels.

Note:
Comparison of Classic Models


Reference:

https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-overview



A company uses Microsoft Dynamics 365 finance and operations apps.

The company plans to use Microsoft Copilot in-app help and guidance to generate responses for internal business processes.

You need to add an additional knowledge source for the business processes. The solution must NOT add new topics to the Copilot agent for the finance and operations apps.

Which knowledge source should you add?

  1. Microsoft Dataverse
  2. a public website
  3. Azure AI Search
  4. a file upload

Answer(s): D

Explanation:

To add an additional knowledge source for internal business processes to the Microsoft Copilot in-app experience for Dynamics 365 finance and operations apps--without creating new topics--you should add File Uploads (such as PDF, Word, or text documents) to the "Copilot for finance and operations apps" agent in Copilot Studio.


Reference:

https://learn.microsoft.com/en-us/dynamics365/fin-ops-core/dev-itpro/copilot/extend-copilot-generative-help



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?

  1. Microsoft Copilot Studio skills
  2. Microsoft Power Platform connectors
  3. custom engine agents

Answer(s): B

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



HOTSPOT

You need to design a shared prompt library that will be used across multiple business units. The solution must meet the following requirements:

Ensure consistent AI responses with reusable formats.

Support governance and version control.

Minimize administrative effort.

Minimize ongoing costs.

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.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Define standardized prompt templates
Ensure consistent AI responses with reusable formats.

To ensure consistent AI responses across multiple business units, your shared prompt library should be built on a foundation of standardized, modular templates that balance centralized governance with unit-specific flexibility.

Box 2: Store prompts in a Git repository
Support governance and version control.

Storing AI prompts in a Git repository allows you to treat prompts as "first-class artifacts" with the same accountability and lifecycle management as source code. For an enterprise solution serving multiple business units, this approach provides the necessary structure for governance, collaboration, and scalability.

1. Repository Organization for Business Units

2. Governance and Version Control Workflow
Branching Strategy: Use a dedicated branch for each experiment or new use case (e.g., feature/marketing- seo-v2) to ensure the main branch remains stable.

Pull Requests (PRs): Mandate PRs for all changes to enable peer reviews. PRs should include descriptions of changes, linked issues, and test results.

Semantic Versioning: Apply tags (e.g., v1.0.1) to mark significant updates, allowing business units to pin their applications to specific, stable prompt versions.

Auditability: Git maintains a full historical record of who changed a prompt, what was modified, and when it occurred.


Reference:

https://www.leeboonstra.dev/prompt-engineering/prompt_engineering_guide6 https://launchdarkly.com/blog/prompt-versioning-and-management



DRAG DROP

A company has a Microsoft Foundry project that uses a single agent and a single prompt to complete a series of tasks.

The agent encounters the following issues:

It frequently produces incomplete results.

It struggles with domain-specific reasoning.

Agent response times are remarkably slow.

You need to recommend a solution to improve the overall performance and accuracy of the agent.

What should you include in the recommendation? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Note: Each correct selection is worth one point.

Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Move to a multi-agent architecture
To improve performance

Moving to a multi-agent architecture in Azure AI Foundry is a highly effective strategy to overcome performance bottlenecks, as single-agent systems often struggle with long-running tasks, leading to high latency and timeout issues. By decomposing complex tasks into smaller, specialized subtasks, you can improve response times through parallel processing and targeted tool usage.

Incorrect:
* Upgrade to a larger generative AI model
To address slow response times in your Microsoft Foundry agent, upgrading to a larger generative AI model is one option, but it may increase latency in some scenarios due to higher processing demands. Instead, a combination of prompt optimization, model selection, and architectural changes in Microsoft Foundry is recommended to improve performance.

Box 2: Add a grounding data source
To improve accuracy

To improve the performance of an agent in a Microsoft Foundry project experiencing incomplete results, weak domain reasoning, and high latency, adding a grounding data source is a highly effective strategy. Grounding connects the Large Language Model (LLM) to verified external data, ensuring responses are accurate, contextual, and less likely to hallucinate.


Reference:

https://developer.microsoft.com/blog/designing-multi-agent-intelligence https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/ground-your-ai-agents-with-knowledge-from- bing-search-microsoft-fabric-sharepoin/4303634



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