Agentic AI Business Solutions Architect (AB-100) Practice Test
Gana confianza para Agentic AI Business Solutions Architect (AB-100). Practica los conceptos, comprende las respuestas y refuerza tus conocimientos pregunta a pregunta.
Probar una preguntaDescripción y detalles del examen
Practice planning, designing, and deploying AI-powered business solutions with this independent practice test for Agentic AI Business Solutions Architect AB-100. You will work through scenarios that connect business goals to AI solution plans, translate requirements into solution designs, and consider how those designs move into deployment. Each question is built to help you reason through decisions rather than memorize isolated facts. When you are ready, use one focused practice session as your next step.
Preguntas de Muestra
Elige una respuesta y consulta la explicación para ver cómo funciona la práctica.
A developer is building an application using the Responses protocol. After creating a conversation, the client needs to upload files so they can be used by the conversation's compute. Which endpoint should the client use?
La respuesta correcta es A. En el protocolo Responses, la plataforma devuelve un ID de sesión que el cliente usa para subir archivos a través del endpoint /files, haciendo que esos archivos estén disponibles para el cómputo de la conversación.
An organization is planning an agentic AI solution and has gathered both business objectives and technical constraints. What must the architect do with these requirements to produce a comprehensive solution design?
La respuesta correcta es A. La responsabilidad del arquitecto es analizar e interpretar los requisitos comerciales y técnicos para diseñar soluciones integrales. Esto garantiza que el diseño aborde tanto las necesidades del negocio como la viabilidad técnica.
A solution architect needs to deploy a model from the model catalog and wants to ensure the deployment supports the entire lifecycle of large language model operations, including integration with platform capabilities. Which deployment method should they use?
La respuesta correcta es B. Managed compute se basa en las capacidades de la plataforma Azure Machine Learning para permitir una integración fluida de los modelos del catálogo a lo largo de todo el ciclo de vida de las operaciones de LLM. Los despliegues serverless, los endpoints en línea de Kubernetes y los endpoints por lotes no proporcionan inherentemente esta integración completa del ciclo de vida.
A software team wants to package an application so that it can be deployed on-premises with minimal changes and includes all necessary components to run. Which approach should they use?
La respuesta correcta es A. La contenorización empaqueta una aplicación o servicio, incluidas sus dependencias y configuración, como una imagen de contenedor que puede desplegarse con poca o ninguna modificación.
An enterprise is planning to upgrade from Azure OpenAI to Azure AI Foundry and intends to phase in non-OpenAI features over several months. Which action must be completed before the upgrade to support this gradual rollout?
La respuesta correcta es A. Para desplegar las funciones que no son de OpenAI de forma gradual, debes actualizar las asignaciones de roles RBAC, las definiciones de Azure Policy y cualquier rol personalizado antes de realizar la actualización. Esto garantiza que el acceso a las nuevas funciones exclusivas de Foundry esté controlado desde el principio.
Qué temas cubre este examen
Usa las ponderaciones publicadas de los dominios para planificar tu estudio. Los resultados de práctica no predicen tu puntuación en el examen de certificación.
01Deploy AI-powered business solutions
This domain involves deploying AI-powered business solutions through recommending tools for monitoring agents, analyzing user feedback and backlogs, and applying artificial intelligence tools to identify issues and perform system tuning.
Temas
- Recommend the process and tools required for monitoring agents
- Analyze backlog and user feedback of AI and agent usage
- Apply AI-based tools to analyze and identify issues and perform tuning
- Monitor agent performance and metrics
- Interpret telemetry data for performance and model tuning
- Recommend the process and metrics to test agents
- Create validation criteria of custom AI models
- Validate effective Copilot prompt best practices
- Design end-to-end test scenarios of AI solutions that use multiple Dynamics 365 apps
- Build the strategy for creating test cases by using Copilot
- Design the ALM process for data used in AI models and agents
- Design the ALM process for Copilot Studio agents, connectors, and actions
- Design the ALM process for Microsoft Foundry Agents service
- Design the ALM process for custom AI models
- Design the ALM process for AI in Dynamics 365 apps for finance and supply chain
- Design the ALM process for AI in Dynamics 365 apps for customer experience and service
- Design security for agents
- Design governance for agents
- Design model security
- Analyze solution and AI vulnerabilities and mitigations, including prompt manipulation
- Review solution for adherence to responsible AI principles
- Validate data residency and movement compliance
- Design access controls on grounding data and model tuning
- Design audit trails for changes to models and data
- Last updated on 06/22/2026
- Purpose of this document
- Updates to the exam
- Skills measured as of July 22, 2026
- Study resources
- Change log
- High contrast
- AI Disclaimer
- Previous Versions
- Contribute
- Privacy
- Consumer Health Privacy
- Terms of Use
- Trademarks
- © Microsoft 2026
Objetivos de aprendizaje
- Recommend the process and tools required for monitoring agents
- Analyze backlog and user feedback of AI and agent usage
- Apply AI-based tools to analyze and identify issues and perform tuning
- Monitor agent performance and metrics
- Interpret telemetry data for performance and model tuning
- Recommend the process and metrics to test agents
- Create validation criteria of custom AI models
- Validate effective Copilot prompt best practices
- Design end-to-end test scenarios of AI solutions that use multiple Dynamics 365 apps
- Build the strategy for creating test cases by using Copilot
- Design the ALM process for data used in AI models and agents
- Design the ALM process for Copilot Studio agents, connectors, and actions
- Design the ALM process for Microsoft Foundry Agents service
- Design the ALM process for custom AI models
- Design the ALM process for AI in Dynamics 365 apps for finance and supply chain
- Design the ALM process for AI in Dynamics 365 apps for customer experience and service
- Design security for agents
- Design governance for agents
- Design model security
- Analyze solution and AI vulnerabilities and mitigations, including prompt manipulation
- Review solution for adherence to responsible AI principles
- Validate data residency and movement compliance
- Design access controls on grounding data and model tuning
- Design audit trails for changes to models and data
- Last updated on 06/22/2026
- Purpose of this document
- Updates to the exam
- Skills measured as of July 22, 2026
- Study resources
- Change log
- High contrast
- AI Disclaimer
- Previous Versions
- Contribute
- Privacy
- Consumer Health Privacy
- Terms of Use
- Trademarks
- © Microsoft 2026
02Design AI-powered business solutions
This domain focuses heavily on designing AI-powered business solutions by establishing business terms for Copilot in Dynamics 365 apps, designing app customizations, and building effective connectors for Dynamics 365 Sales.
Temas
- Design business terms for Copilot in Dynamics 365 apps for customer experience and service
- Design customizations of Copilot in Dynamics 365 apps for customer experience and service
- Design connectors for Copilot in Dynamics 365 Sales
- Design agents for integration with Dynamics 365 Contact Center channels
- Design task agents
- Design autonomous agents
- Design prompt and response agents
- Propose Foundry Tools for a given requirement
- Propose code-first generative pages and the use of an agent feed for apps
- Design topics for Copilot Studio, including fallback
- Design data processing for AI models and grounding
- Design a business process to include AI components in a Power Apps canvas app
- Apply the Microsoft Power Platform Well-Architected Framework to intelligent application workloads
- Determine when to use standard natural language processing, conversational language understanding, or generative AI orchestration in Copilot Studio
- Design agents and agent flows with Copilot Studio
- Design prompt actions in Copilot Studio
- Design AI solutions by using custom models in Microsoft Foundry
- Design agents in Microsoft 365 Copilot
- Design agent extensibility in Copilot Studio
- Design agent extensibility with Model Context Protocol in Copilot Studio
- Design agents to automate tasks in apps and websites by using Computer Use in Copilot Studio
- Design agent behaviors in Copilot Studio, including reasoning and voice mode
- Optimize solution design by using agents in Microsoft 365, including Teams and SharePoint
- Orchestrate AI features in Dynamics 365 apps for finance and supply chain
- Orchestrate AI features in Dynamics 365 apps for customer experience and service
- Propose Microsoft 365 agents for business scenarios
- Orchestrate the configuration of Microsoft 365 Copilot for Sales and Microsoft 365 Copilot for Service
- Propose Microsoft Power Platform AI features, including AI hub
- Design interoperability of the finance and operations agent chats to use additional knowledge sources
- Recommend the process of adding knowledge sources to in-app help and guidance for Dynamics 365 Finance or Dynamics 365 Supply Chain Management apps
Objetivos de aprendizaje
- Design business terms for Copilot in Dynamics 365 apps for customer experience and service
- Design customizations of Copilot in Dynamics 365 apps for customer experience and service
- Design connectors for Copilot in Dynamics 365 Sales
- Design agents for integration with Dynamics 365 Contact Center channels
- Design task agents
- Design autonomous agents
- Design prompt and response agents
- Propose Foundry Tools for a given requirement
- Propose code-first generative pages and the use of an agent feed for apps
- Design topics for Copilot Studio, including fallback
- Design data processing for AI models and grounding
- Design a business process to include AI components in a Power Apps canvas app
- Apply the Microsoft Power Platform Well-Architected Framework to intelligent application workloads
- Determine when to use standard natural language processing, conversational language understanding, or generative AI orchestration in Copilot Studio
- Design agents and agent flows with Copilot Studio
- Design prompt actions in Copilot Studio
- Design AI solutions by using custom models in Microsoft Foundry
- Design agents in Microsoft 365 Copilot
- Design agent extensibility in Copilot Studio
- Design agent extensibility with Model Context Protocol in Copilot Studio
- Design agents to automate tasks in apps and websites by using Computer Use in Copilot Studio
- Design agent behaviors in Copilot Studio, including reasoning and voice mode
- Optimize solution design by using agents in Microsoft 365, including Teams and SharePoint
- Orchestrate AI features in Dynamics 365 apps for finance and supply chain
- Orchestrate AI features in Dynamics 365 apps for customer experience and service
- Propose Microsoft 365 agents for business scenarios
- Orchestrate the configuration of Microsoft 365 Copilot for Sales and Microsoft 365 Copilot for Service
- Propose Microsoft Power Platform AI features, including AI hub
- Design interoperability of the finance and operations agent chats to use additional knowledge sources
- Recommend the process of adding knowledge sources to in-app help and guidance for Dynamics 365 Finance or Dynamics 365 Supply Chain Management apps
03Plan AI-powered business solutions
This domain covers planning AI-powered business solutions by assessing agents in task automation and data analytics, reviewing data for grounding, and organizing business solution data for other artificial intelligence systems.
Temas
- Assess the use of agents in task automation, data analytics, and decision-making
- Review data for grounding, including accuracy, relevance, timeliness, cleanliness, and availability
- Organize business solution data to be available for other AI systems
- Implement the AI adoption process from the Cloud Adoption Framework for Azure
- Design the strategy for building AI and agents in business solutions
- Design a multi-agent solution by using platforms such as Microsoft 365 Copilot, Copilot Studio, and Microsoft Foundry
- Develop the use cases for prebuilt agents in the solution
- Define the solution rules and constraints when building AI components with Copilot Studio, Microsoft Foundry and Foundry Tools
- Determine the use of generative AI and knowledge sources in agents built with Copilot Studio
- Determine when to build custom agents or extend Microsoft 365 Copilot
- Determine when custom AI models should be created
- Provide guidelines for creating a prompt library
- Develop the use cases for customized small language models for the solution
- Provide prompt engineering guidelines and techniques for AI-powered business solutions
- Include the elements of the Microsoft AI Center of Excellence
- Design AI solutions that use multiple Dynamics 365 apps
- Select ROI criteria for AI-powered business solutions, including the total cost of ownership
- Create an ROI analysis for the proposed AI solution for a business process
- Analyze whether to build, buy, or extend AI components for business solutions
- Implement a model router to intelligently route requests to the most suitable model
Objetivos de aprendizaje
- Assess the use of agents in task automation, data analytics, and decision-making
- Review data for grounding, including accuracy, relevance, timeliness, cleanliness, and availability
- Organize business solution data to be available for other AI systems
- Implement the AI adoption process from the Cloud Adoption Framework for Azure
- Design the strategy for building AI and agents in business solutions
- Design a multi-agent solution by using platforms such as Microsoft 365 Copilot, Copilot Studio, and Microsoft Foundry
- Develop the use cases for prebuilt agents in the solution
- Define the solution rules and constraints when building AI components with Copilot Studio, Microsoft Foundry and Foundry Tools
- Determine the use of generative AI and knowledge sources in agents built with Copilot Studio
- Determine when to build custom agents or extend Microsoft 365 Copilot
- Determine when custom AI models should be created
- Provide guidelines for creating a prompt library
- Develop the use cases for customized small language models for the solution
- Provide prompt engineering guidelines and techniques for AI-powered business solutions
- Include the elements of the Microsoft AI Center of Excellence
- Design AI solutions that use multiple Dynamics 365 apps
- Select ROI criteria for AI-powered business solutions, including the total cost of ownership
- Create an ROI analysis for the proposed AI solution for a business process
- Analyze whether to build, buy, or extend AI components for business solutions
- Implement a model router to intelligently route requests to the most suitable model