Microsoft Azure AI Fundamentals (AI - 901) Exam Questions 2026 Microsoft Azure AI Fundamentals (AI - 901) Questions 2026 Contains 320+ exam questions to pass the exam in first attempt. SkillCertPro offers real exam questions for practice for all major IT certifications. • For a full set of 340 questions. Go to https://skillcertpro.com/product/microsoft - azure - ai - fundamentals - ai - 901 - exam - questions/ • SkillCertPro offers detailed explanations to each question which helps to understand the concepts better. • It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. • SkillCertPro updates exam questions every 2 weeks. • You will get life time access and life time free updates • SkillCertPro assures 100% pass guarantee in first attempt. Below are the free 10 sample questions. Question 1: You need one approach that can extract structured information from documents, images, call recordings, and videos. Which option is the best fit? A.OCR B. Sentiment analysis C. Azure Content Understanding D. Text to Speech Answer: C Explanation: Azure Content Understanding is the strongest answer because it is designed to derive structured insights from multiple content types, including documents, images, audio, and video. Microsoft describes it as a multimodal extraction service that processes these inputs into user - defined structured outputs, which matches the requirement in the question. OCR is narrower because it focuses on reading text from images or documents. Sentiment analysis is limited to text opinion detection, and Text to Speech produces audio from text. None of those cover the full cross - modal extraction requirement as directly as Content Understanding. I ncorrect: A. OCR is a strong distractor because it is genuinely useful for extracting text from images. However, it does not cover audio or video extraction, and it is much narrower than the multimodal requirement in the stem. OCR is helpful for text - in - image scenarios, but not as the one best answer for all listed modalities. B. Sentiment analysis works on text to identify attitudes or opinions. It does not extract structured information from images, recordings, or video streams. This option confuses text understanding with multimodal information extraction. D. Text to Speech is part of speech synthesis and is used to generate spoken audio from text. It does not analyze source content to pull out fields, entities, or structured facts. This makes it the opposite of the extraction task being asked for. Question 2: You want to define agent instructions, attach tools, and test multi - turn behavior in Foundry without writing code. Which portal experience is the best fit? Select only one answer. A. Agents playground B. Model catalog C. Management center D. Content safety dashboard Answer: A Explanation: The Agents playground is the Foundry portal experience designed to explore, prototype, and test agents without running code. Microsoft ’ s documentation says it lets you configure instructions and persona, attach tools, add knowledge sources, test multi - turn conversations, and iterate before deploying. The Agent Service overview also describes prompt agents as no - code agents you can create in the Foundry portal, then test in the agents playground. That is exactly the workflow described in the question, so Agents playground is the best answer. I ncorrect: B. The Model catalog is where you browse or select models, not where you configure and test a single prompt agent with tools and instructions. Model selection is important before agent creation, but it is not the main portal surface for running multi - turn agent tests. The question is about building and testing an agent experience, not selecting a model family. C. Management center is used for project and resource management tasks rather than direct agent behavior testing. It is relevant for connected resources, administration, and setup, but it does not provide the core agent chat - and - iterate experience described in the docs. That makes it a plausible administrative distractor, not the correct portal workspace. D. Content safety dashboards and controls are important for governance and safer outputs, but they are not the portal experience used to define an agent, attach tools, and run multi - turn test chats. Those responsibilities sit with the Agents playground and the broader agent lifecycle tooling. This option confuses safety configuration with agent development workflow. Question 3 : Which THREE pairs are correctly matched? (Select THREE.) A. System prompt — sets assistant behavior and rules B. User prompt — supplies retrieved source documents automatically C. Grounding data — external data used to anchor responses D. Context — hidden deployment policy configured outside the conversation E. Grounding data — controls token sampling randomness F. User prompt — contains the end user ’ s request Answer: A, C and F Explanation: A system prompt guides the model ’ s behavior, tone, constraints, and task framing. A user prompt is the actual request from the end user. Grounding data refers to external content, such as indexed or retrieved material, that helps keep the response tied to relevant source information instead of relying only on the model ’ s pretraining. Context is broader than a hidden deployment policy. In practice, context can include prior turns, instructions, and relevant conversation state that the model can consider. Grounding data is also not a sampling control; it is a relevance and accuracy aid, especially in RAG - style solutions where source content is supplied to improve answer quality. Why the other options are incorrect: B. This is plausible because retrieved documents do often appear alongside a user request in grounded solutions. However, retrieved documents are grounding data or retrieved context, not the user prompt itself. The user prompt is the human ’ s instruction or question. Mixing these concepts leads to confusion about where the model ’ s task ends and where external knowledge begins. D. This is plausible because system - level controls can influence behavior. However, context is not simply a hidden deployment policy configured elsewhere. Context usually refers to the relevant information included in the conversation or request window, such as prior messages or supporting content that the model can use when generating an answer. E. This is incorrect because randomness controls belong to model settings such as temperature or top_p, not to grounding data. Grounding data is about supplying external facts or retrieved content so the response is better anchored to source material. It improves factual alignment, not stochastic sampling behavior. Question 4 : You deployed gpt - realtime in Microsoft Foundry and want to test voice input and spoken responses in the portal before writing app code. Which playground should you use? A. Audio playground B. Chat playground C. Agents playground D. Vision playground Answer: A Explanation: The correct answer is Audio playground. Microsoft ’ s GPT Realtime documentation says you can interact with a deployed gpt - realtime model in the Foundry portal Audio playground or through the Realtime API, and it explicitly notes that the Chat playground does not support the gpt - realtime model. This makes the Audio playground the right portal experience when you want to test spoken prompts and responses before building an application. It is designed for the real - time speech - and - audio interaction pattern used by these deployed models. I ncorrect: B. Chat playground is a tempting answer because it is the standard place for many text - based model tests. However, Microsoft explicitly states that the Chat playground does not support the gpt - realtime model. For spoken prompt and audio response testing, Audio playground is the supported choice. C. Agents playground is used for building and testing agents, especially when you are configuring instructions, tools, versions, and agent behavior. That is a different experience from directly testing a deployed realtime audio model in the portal. The stem asks for spoken prompts with a deployed multimodal model, not an agent build workflow. D. Vision playground is incorrect because the scenario is about speech input and spoken output, not image - centric testing. Realtime audio testing belongs in the Audio playground, which is specifically documented for deployed gpt - realtime models. Choosing Vision playground would mismatch the modality and the testing workflow. Question 5 : In Microsoft Foundry, you want to convert document chunks into vectors for semantic search and retrieval. Which model type is the best fit? Select only one answer. A. Chat completion model B. Image generation model C. Embedding model D. Speech model Answer: C Explanation: An embedding model is designed to convert text or other content into numeric vector representations that can be compared for similarity. That makes it the right choice for semantic search and retrieval scenarios, where the goal is to match meaning rather than exact keywords. Microsoft Learn s model - selection guidance says to choose models based on task fit, such as chat, reasoning, embedding, RAG, or multimodal processing. Foundry guidance also highlights model catalogs, leaderboards, and deployment workflows that help you select the right model by capability rather than by brand name alone. I ncorrect: A. A chat completion model is optimized for conversational generation, instruction following, and text responses. It can participate in a search solution, but it is not the primary model type used to create similarity vectors for retrieval. The question is specifically about vectorizing document chunks, which points to embeddings. B. An image generation model creates new visual outputs from prompts. That capability is unrelated to transforming text chunks into numeric embeddings for search. It is a plausible distractor because it is a model capability, but it is the wrong capability for retrieval. D. A speech model is used for scenarios involving spoken input, transcription, synthesis, or related audio tasks. It does not serve as the primary model type for semantic vector search over text chunks. The required capability here is embedding generation, not speech processing. • For a full set of 340 questions. Go to https://skillcertpro.com/product/microsoft - azure - ai - fundamentals - ai - 901 - exam - questions/ • SkillCertPro offers detailed explanations to each question which helps to understand the concepts better. • It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. • SkillCertPro updates exam questions every 2 weeks. • You will get life time access and life time free updates • SkillCertPro assures 100% pass guarantee in first attempt. Question 6 : You are building a simple voice - enabled prompt app. A user speaks a request, the model generates a text answer, and the app reads the answer aloud. Which TWO steps belong in that flow? (Select TWO.) A. Convert spoken input to text before prompt submission B. Use OCR on the microphone stream C. Convert the final text response to speech D. Run object detection on the audio signal E. Use sentiment analysis as the required output step Answer: A and C Explanation: A simple speech - and - prompt flow usually starts by converting spoken input into text and ends by converting the model ’ s text response back into audio. That pattern matches the roles of speech to text and text to speech in Azure Speech, and it is also consistent with Microsoft ’ s voice - oriented tooling for prompt experiences. The key idea is that prompt - based model interaction is text - centered even when the user begins with speech. The speech layer handles input and output, while the generative model or prompt flow operates on text in the middle of the application. I ncorrect: B. OCR is used to read printed or handwritten text from images and documents. A microphone stream contains audio, not image data, so OCR is the wrong capability for this part of the flow. This distractor is plausible because OCR is also an extraction tool, but it belongs to vision rather than speech. D. Object detection is a computer vision task for identifying objects and often returning their positions in an image. It has nothing to do with spoken audio input or prompt submission. This option deliberately mixes a vision workload into a speech - and - text application flow. E. Sentiment analysis is a useful optional text - analysis capability, but it is not a required step for turning a spoken request into a spoken answer. A basic voice prompt app can work without classifying sentiment at all. The question asks for the core flow, not for extra enrichment steps that might be added later. Question 7 : You need to generate a new product mockup from a text prompt in Microsoft Foundry. Which deployment should you choose? Select only one answer. A. gpt - 4o - mini B. Azure AI Vision C. gpt - image - 1 D. Azure Language Answer: C Explanation: gpt - image - 1 is the correct choice because Microsoft ’ s current image generation guidance for Foundry points users to the GPT - image family for creating new images from prompts. The image generation documentation explicitly states that image generation models create images from user - provided prompts and that gpt - image - 1 - series deployments are the image - generation path to use. This distinction matters because not every model or service that can process text is designed to generate new visual output. In Foundry, image creation is a model capability choice, so you need an image - generation model rather than a vision - analysis or text - analysis service. I ncorrect: A. gpt - 4o - mini is a useful multimodal model for many chat and reasoning tasks, but it is not itself the image - generation deployment named in the image model guidance. The Responses API can call image - generation tools in some workflows, yet the actual image generation capability is provided by gpt - image - 1 - series models. This makes the option plausible but not the most direct answer to the deployment question. B. Azure AI Vision is designed for analyzing images, such as OCR, captioning, and object - related understanding tasks. It does not generate new synthetic images from a text prompt. This option intentionally confuses image understanding with image creation. D. Azure Language provides NLP features such as sentiment analysis, key phrase extraction, entity recognition, and summarization. It is a text analysis service, not an image - generation deployment. The option is credible as an Azure AI service name, but it targets the wrong modality and workload. Question 8 : You need to process invoices and tax forms from many templates and return fields such as invoice number, dates, totals, and line items in a structured schema. Which option is the best fit? Select only one answer. A. Azure AI Vision B. Azure Content Understanding C. Azure AI Search D. Multimodal model Answer: B Explanation: Azure Content Understanding is built for turning unstructured documents and forms into structured, machine - readable outputs. Microsoft ’ s document overview says document analyzers can extract essential information, fields, and relationships from diverse documents and forms, which matches this scenario directly. The analyzer reference also explains that analyzers combine content extraction, AI - powered analysis, and structured output into a reusable configuration. That is why Content Understanding is a better fit than a general multimodal prompt when the goal is repeatable schema - based extraction across many document templates. I ncorrect: A. Azure AI Vision can help with visual understanding tasks, but this question is specifically about structured extraction from varied documents and forms. Microsoft positions Content Understanding document analyzers as the modality - specific tool for extracting fields and relationships with customizable analyzers. Vision is therefore a plausible confusion, but not the best answer here. C. Azure AI Search is used for indexing and retrieval, not as the primary document field - extraction tool described in this scenario. Search can consume structured outputs later in a pipeline, but it is not the service that performs the document and form extraction itself. The question asks for the extraction tool, not the downstream retrieval layer. D. A multimodal model can interpret document images or answer questions about them, but the docs for Content Understanding emphasize schema - defined structured outputs and analyzers tailored to extraction tasks. For invoice numbers, totals, and line items across many formats, a structured extraction service is the cleaner and more reliable fit. This is exactly the distinction AI - 901 expects candidates to recognize. Question 9 : A retailer wants to analyze shelf photos and return structured fields such as product count, brand presence, and out - of - stock indicators. Which approach is the best fit? Select only one answer. A. Azure Content Understanding image analyzer B. Multimodal vision prompt C. Azure AI Speech D.OCR - only pipeline Answer: A Explanation: Microsoft ’ s image - overview documentation says Content Understanding lets you define schemas with fields, descriptions, and output types, then analyze images into structured data. It also lists shelf analysis and inventory management as a direct image - use case, which makes an image analyzer the strongest fit for this scenario. The analyzer reference reinforces this by describing analyzers as reusable configurations that combine extraction, AI analysis, and structured output. That is exactly what a shelf - photo workflow needs when the business wants specific fields rather than a general free - form description. I ncorrect: B. A multimodal vision prompt can describe what is visible in an image, but that is not the same as a schema - defined extraction workflow. The image - overview page positions Content Understanding as the tool for structured outputs that can feed downstream systems. A free - form prompt is therefore plausible, but weaker when the output must be structured and repeatable. C. Azure AI Speech is designed for audio workloads, not image extraction. The modality mismatch makes it clearly wrong once you identify that the input is shelf photos rather than spoken content. Microsoft ’ s Content Understanding image solution is the documented image - specific path. D. OCR - only pipelines focus on extracted text, while this scenario includes broader visual understanding such as product count and shelf state. The image - overview documentation emphasizes structured extraction from unstructured images for business workflows like shelf analysis. OCR might capture labels, but it is not the best overall fit for the full task described. Question 10 : Your team receives a mixed mailbox of purchase orders, invoices, and other procurement - related documents. You want one prebuilt analyzer that is already tuned for that business category. Which analyzer should you choose? Select only one answer. A. prebuilt - invoice B. prebuilt - purchaseOrder C. prebuilt - procurement D. prebuilt - documentSearch Answer: C Explanation: prebuilt - procurement is the best fit because Microsoft documents it as the analyzer for purchase orders, invoices, and procurement - related documents. That makes it broader and more appropriate than an invoice - only or purchase - order - only analyzer when the incoming content is mixed across procurement document types. This is exactly the kind of “ best fit ” distinction the prebuilt analyzer catalog is designed to support. Microsoft separates domain - specific analyzers, such as procurement and invoice analyzers, from RAG - oriented analyzers like prebuilt - documentSearch, which are optimized for search and knowledge - ingestion scenarios instead of specialized business - field extraction. I ncorrect: A. prebuilt - invoice is a real analyzer and is plausible because invoices are part of the mailbox. However, Microsoft lists it specifically for invoices, utility bills, sales orders, and purchase orders, while prebuilt - procurement is the broader analyzer for procurement - related documents as a category. Since the mailbox is mixed, invoice - specific tuning is narrower than the scenario requires. B. prebuilt - purchaseOrder is also plausible because purchase orders are explicitly mentioned in the scenario. But it is still too narrow for a stream that also contains invoices and other procurement documents. The question asks for one prebuilt analyzer suited to the broader business scenario, not one analyzer for just a single document subtype. D. prebuilt - documentSearch is the best distractor because it is useful for document ingestion and RAG workflows. However, Microsoft describes it as a RAG - oriented analyzer that extracts markdown, layout, figures, and summaries for retrieval scenarios, not as the best domain - specific choice for procurement field extraction. It is powerful, but it is not the most specialized answer for this business case. • For a full set of 340 questions. Go to https://skillcertpro.com/product/microsoft - azure - ai - fundamentals - ai - 901 - exam - questions/ • SkillCertPro offers detailed explanations to each question which helps to understand the concepts better. • It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. • SkillCertPro updates exam questions every 2 weeks. • You will get life time access and life time free updates • SkillCertPro assures 100% pass guarantee in first attempt.