Microsoft Azure AI Fundamentals (AI-901) Practice Test
Gana confianza para Microsoft Azure AI Fundamentals (AI-901). Practica los conceptos, comprende las respuestas y refuerza tus conocimientos pregunta a pregunta.
Probar una preguntaDescripción y detalles del examen
Through this practice test, you can build a working understanding of core AI concepts and how Microsoft Foundry supports AI solution implementation. You will connect ideas like identifying AI capabilities and using Microsoft Foundry to practical tasks, such as recognizing when to apply AI and how to begin building solutions. When you are ready, take one focused practice set and review each explanation as a small, manageable next step.
Temario 1.0
Preguntas de Muestra
Elige una respuesta y consulta la explicación para ver cómo funciona la práctica.
234 de 242 respuestas incluyen una referencia verificable.
You are configuring network access for a Foundry Tools resource. You need to grant access to a virtual network that is in a different Microsoft Entra tenant. What must be true?
A company deploys the gpt-6-astra model in Microsoft Foundry. During inference, the safety systems detect elevated risk. Which action may the model take as part of enhanced safety controls?
A company uses a provisioned deployment of a language model that is scheduled for retirement. What should the company do?
La respuesta correcta es B. Las implementaciones provisionadas no se actualizan automáticamente, por lo que los clientes deben migrar manualmente. La recomendación es no esperar a que Microsoft anuncie un reemplazo oficial antes de evaluar modelos más recientes; en su lugar, comience a evaluar a medida que estén disponibles nuevos modelos.
A Microsoft certification exam is offered in English and several localized languages. When the exam content is updated, which version is updated first?
A candidate is reviewing the skills measured for the AI-901 exam. They notice that each skill is followed by a list of bullets. What is the purpose of these bullets?
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.
01Implement AI solutions by using Microsoft Foundry
Temas
- Create effective system and user prompts for generative AI models
- Deploy a model and interact with it in the Foundry portal
- Create a lightweight chat client application by using the Foundry SDK
- Create and test a single-agent solution in the Foundry portal
- Create a lightweight client application for an agent
- Build a lightweight application that includes text analysis
- Respond to spoken prompts by using a deployed multimodal model
- Build a lightweight application by using Azure Speech in Foundry Tools
- Interpret visual input in prompts by using a deployed multimodal model
- Create new visual outputs by using generative models
- Build a lightweight application that includes vision capabilities
- Extract information from documents and forms by using Azure Content Understanding in Foundry Tools
- Extract information from images by using Content Understanding
- Extract information from audio and video by using Content Understanding
- Build a lightweight application with information extraction capabilities by using Content Understanding
- Last updated on 07/13/2026
- Purpose of this document
- Updates to the exam
- Skills measured as of April 15, 2026
- Study resources
- High contrast
- AI Disclaimer
- Previous Versions
- Contribute
- Privacy
- Consumer Health Privacy
- Terms of Use
- Trademarks
- © Microsoft 2026
Objetivos de aprendizaje
- Create effective system and user prompts for generative AI models
- Deploy a model and interact with it in the Foundry portal
- Create a lightweight chat client application by using the Foundry SDK
- Create and test a single-agent solution in the Foundry portal
- Create a lightweight client application for an agent
- Build a lightweight application that includes text analysis
- Respond to spoken prompts by using a deployed multimodal model
- Build a lightweight application by using Azure Speech in Foundry Tools
- Interpret visual input in prompts by using a deployed multimodal model
- Create new visual outputs by using generative models
- Build a lightweight application that includes vision capabilities
- Extract information from documents and forms by using Azure Content Understanding in Foundry Tools
- Extract information from images by using Content Understanding
- Extract information from audio and video by using Content Understanding
- Build a lightweight application with information extraction capabilities by using Content Understanding
- Last updated on 07/13/2026
- Purpose of this document
- Updates to the exam
- Skills measured as of April 15, 2026
- Study resources
- High contrast
- AI Disclaimer
- Previous Versions
- Contribute
- Privacy
- Consumer Health Privacy
- Terms of Use
- Trademarks
- © Microsoft 2026
02Identify AI concepts and capabilities
Temas
- Describe considerations for fairness in an AI solution
- Describe considerations for reliability and safety in an AI solution
- Describe considerations for privacy and security in an AI solution
- Describe considerations for inclusiveness in an AI solution
- Describe considerations for transparency in an AI solution
- Describe considerations for accountability in an AI solution
- Describe how generative AI models work
- Identify an appropriate AI model, based on capabilities
- Identify appropriate model deployment options and configuration parameters
- Identify scenarios for common AI workloads, including generative and agentic AI, text analysis, speech, computer vision, and information extraction
- Describe common text analysis techniques, including keyword extraction, entity detection, sentiment analysis, and summarization
- Identify features and capabilities of speech recognition and speech synthesis
- Identify features and capabilities of computer vision and image-generation models
- Identify techniques to extract information from text, images, audio, and videos
Objetivos de aprendizaje
- Describe considerations for fairness in an AI solution
- Describe considerations for reliability and safety in an AI solution
- Describe considerations for privacy and security in an AI solution
- Describe considerations for inclusiveness in an AI solution
- Describe considerations for transparency in an AI solution
- Describe considerations for accountability in an AI solution
- Describe how generative AI models work
- Identify an appropriate AI model, based on capabilities
- Identify appropriate model deployment options and configuration parameters
- Identify scenarios for common AI workloads, including generative and agentic AI, text analysis, speech, computer vision, and information extraction
- Describe common text analysis techniques, including keyword extraction, entity detection, sentiment analysis, and summarization
- Identify features and capabilities of speech recognition and speech synthesis
- Identify features and capabilities of computer vision and image-generation models
- Identify techniques to extract information from text, images, audio, and videos