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Microsoft AI-900 : Microsoft Azure AI Fundamentals
Exam Code: AI-900
Exam Name: Microsoft Azure AI Fundamentals
Updated: Oct 17, 2026
Q & A: 336 Questions and Answers
AI-900 Free Demo download
About Microsoft AI-900 Exam
Earning the Microsoft AI-900 certification can be a significant advantage for individuals who want to work in AI-related roles or want to progress in their careers. Microsoft Azure AI Fundamentals certification demonstrates the individual's expertise in AI and machine learning concepts and their ability to apply them to business scenarios using Microsoft Azure services. Additionally, the certification can be useful for individuals who want to start their own AI-related businesses or want to contribute to the development of AI-based solutions in their organizations.
Microsoft AI-900 exam, also known as the Microsoft Azure AI Fundamentals, is a certification exam that tests the fundamental knowledge and skills required to understand artificial intelligence (AI) and its use in Microsoft Azure. AI-900 exam is designed for individuals who have a basic understanding of AI and machine learning concepts and want to gain a deeper understanding of how these concepts can be applied to Microsoft Azure services.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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The AI-900 exam covers a wide range of topics related to AI and ML such as Understanding AI workloads and considerations, Understanding fundamental principles of machine learning on Azure, Understanding features of computer vision workloads on Azure, Understanding features of Natural Language Processing (NLP) workloads on Azure, Understanding features of conversational AI workloads on Azure, and many more.
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Identify common NLP tasks - Describe Azure capabilities for NLP |
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities |
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Identify common computer vision tasks - Describe Azure capabilities for computer vision |
Microsoft Azure AI Fundamentals Sample Questions:
Question #1
What is an example of a Microsoft responsible Al principle?
- A. Al systems should protect the interests of developers.
- B. Al systems should be secure and respect privacy.
- C. Al systems should make personal details accessible.
- D. Al systems should be in the public domain.
Correct Answer: B 🗳️
Explanation: Only visible for ITexamReview members. You can sign-up / login (it's free).
Question #2
You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer.
Which service should you integrate with the web app to meet the goal?
- A. Face
- B. Azure Al Language Service
- C. Azure Al Custom Vision
- D. Azure Al Translator
Correct Answer: C 🗳️
Explanation: Only visible for ITexamReview members. You can sign-up / login (it's free).
Question #3
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service and has nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna-bot LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
Question #4
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
These answers align with the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure."
1. A webchat bot can interact with users visiting a website # Yes
This statement is true. The Azure Bot Service allows developers to create intelligent chatbots that can be integrated into a webchat interface. This enables visitors to interact with the bot directly from a website, asking questions and receiving automated responses. This is a typical use case of conversational AI, where natural language processing (NLP) is used to interpret and respond to user input conversationally.
2. Automatically generating captions for pre-recorded videos is an example of conversational AI # No This statement is false. Automatically generating captions from video content is an example of speech-to-text (speech recognition) technology, not conversational AI. While it uses AI to convert spoken words into text, it lacks the two-way interactive communication characteristic of conversational AI. This task is typically handled by the Azure AI Speech service, which transcribes spoken content.
3. A smart device in the home that responds to questions such as "What will the weather be like today?" is an example of conversational AI # Yes This statement is true. Smart home assistants that engage in dialogue with users are powered by conversational AI. These devices use speech recognition to understand spoken input, natural language understanding (NLU) to determine intent, and speech synthesis (text-to-speech) to respond audibly. This represents the full conversational AI loop, where machines communicate naturally with humans.
Question #5
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The Azure AI Language Service includes several natural language processing features, such as question answering, language understanding, entity recognition, sentiment analysis, and more. Each feature serves a distinct purpose, and understanding their differences is key to selecting the correct AI workload.
* "You can use Azure AI Language Service ' s question answering to query an Azure SQL database." - NOThe question answering feature is designed to retrieve answers from text-based knowledge sources (for example, FAQs, documents, or website content). It cannot directly query a database such as Azure SQL. Querying databases requires Azure Cognitive Search, Azure OpenAI, or custom integration using application logic, not the question answering model.
* "You should use Azure AI Language Service ' s question answering when you want a knowledge base to provide the same answer to different users who submit similar questions." - YESThis is the primary use case of question answering. It allows developers to build a knowledge base (KB) of predefined question-answer pairs or extract answers from documents. When users submit semantically similar questions (e.g., "What are your office hours?" or "When are you open?"), the service returns the same consistent answer.
* "Azure AI Language Service ' s question answering can determine the intent of a user utterance." - NODetermining user intent is part of the Language Understanding (LUIS) capability, not question answering. LUIS models map natural language inputs to intents and entities, typically used in bots or applications that execute tasks (like booking a meeting or checking weather).
Hence, correct answers are: No, Yes, No - aligning with the AI-900 official study guide and Microsoft Learn module "Identify Azure AI Language capabilities."
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