Microsoft AI-901 : Microsoft Azure AI Fundamentals

  • Exam Code: AI-901
  • Exam Name: Microsoft Azure AI Fundamentals
  • Updated: Oct 12, 2026
  • Q & A: 132 Questions and Answers

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About Microsoft AI-901 Exam

Dreams of a higher standard of living need practical mechanics. For many professionals, that mechanism is the Microsoft Azure AI Fundamentals certification — and BraindumpsPrep supplies the preparation: 132 practice questions, a free demo, and McAfee-secured checkout.

Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Describe Artificial Intelligence workloads and considerations (15-20%)15-20%- Identify guiding principles for responsible AI
  • 1. Describe considerations for fairness in an AI solution
  • 2. Describe considerations for privacy and security in an AI solution
  • 3. Describe considerations for reliability and safety in an AI solution
  • 4. Describe considerations for inclusiveness in an AI solution
  • 5. Describe considerations for transparency in an AI solution
  • 6. Describe considerations for accountability in an AI solution
- Identify features of common AI workloads
  • 1. Identify features of computer vision workloads
  • 2. Identify features of generative AI workloads
  • 3. Identify features of knowledge mining workloads
  • 4. Identify features of Natural Language Processing (NLP) workloads
  • 5. Identify features of content moderation and moderation workloads
  • 6. Identify features of document intelligence workloads
Describe fundamental principles of machine learning on Azure (15-20%)15-20%- Identify common machine learning techniques
  • 1. Identify regression machine learning scenarios
  • 2. Identify features of deep learning techniques
  • 3. Identify classification machine learning scenarios
  • 4. Identify clustering machine learning scenarios
- Describe core machine learning concepts
  • 1. Identify features and labels for training data in machine learning
  • 2. Describe how training and validation datasets are used in machine learning
- Describe Azure Machine Learning capabilities
  • 1. Describe model management and deployment capabilities in Azure Machine Learning
  • 2. Describe capabilities of automated machine learning
  • 3. Describe data and compute services for data science and machine learning
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)15-20%- Identify features of common NLP workload scenarios
  • 1. Identify features and uses for entity recognition
  • 2. Identify features and uses for sentiment analysis
  • 3. Identify features and uses for key phrase extraction
  • 4. Identify features and uses for translation
  • 5. Identify features and uses for speech recognition and synthesis
  • 6. Identify features and uses for language modeling
- Identify Azure tools and services for NLP workloads
  • 1. Describe capabilities of the Azure AI Speech service
  • 2. Describe capabilities of the Azure AI Language service
Describe features of generative AI workloads on Azure (20-25%)20-25%- Identify generative AI services and capabilities in Microsoft Azure
  • 1. Describe features and capabilities of Azure AI Foundry
  • 2. Describe features and capabilities of Azure AI Foundry model catalog
  • 3. Describe features and capabilities of Azure OpenAI service
- Identify features of generative AI solutions
  • 1. Identify features of generative AI models
  • 2. Identify responsible AI considerations for generative AI
  • 3. Identify common scenarios for generative AI
Describe features of computer vision workloads on Azure (15-20%)15-20%- Identify Azure tools and services for computer vision tasks
  • 1. Describe capabilities of the Azure AI Face detection service
  • 2. Describe capabilities of the Azure AI Vision service
- Identify common types of computer vision solution
  • 1. Identify features of image classification solutions
  • 2. Identify features of optical character recognition (OCR) solutions
  • 3. Identify features of image analysis solutions
  • 4. Identify features of semantic segmentation solutions
  • 5. Identify features of object detection solutions
  • 6. Identify features of face detection and identification solutions

AI-901 Exam: Frequently Asked Questions

How do I register for the AI-901 exam?

Online (remote proctored via Pearson VUE) or at a Pearson VUE test center Book the Microsoft Azure AI Fundamentals exam through these official channels:

    Once booked, your BraindumpsPrep practice questions arrive by email within 1 minute, so preparation starts the same day.

    What score passes the AI-901 exam, and what is the fee?

    The passing score for the Microsoft Azure AI Fundamentals exam is 700 / 1000, and registration costs USD 99. A retake means the same fee again — a sound reason to test your readiness with the BraindumpsPrep engines before committing to a date.

    What is the BraindumpsPrep refund policy if I fail the AI-901 exam?

    BraindumpsPrep refunds you in full if you take the Microsoft Azure AI Fundamentals exam within 60 days of purchase and do not pass. The policy does not apply if the exam is taken within 3 days of purchase, if the exam was never actually taken, or to free materials or expired orders, and the candidate name must match the payer name. Submit a scan of your enrollment slip and the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. As an alternative, exchange for two free exam products of equal value while keeping your original update service. Delivery is by email within 1 minute — contact support if nothing arrives within 2 hours.

    What training does Microsoft recommend for the AI-901 exam?

    Microsoft recommends these training resources for the Microsoft Azure AI Fundamentals exam:

      After the training, the 132 practice questions from BraindumpsPrep let you make full use of sporadic time — short sessions that steadily convert study into readiness.

      How many questions does the AI-901 exam include, and how much time is allowed?

      The Microsoft Azure AI Fundamentals exam presents 40-60 with 45-60 available. That pace is demanding — rehearse it in the timed mode of the BraindumpsPrep Desktop Test Engine until it feels familiar.

      What are the main domains of the AI-901 exam?

      The official Microsoft Azure AI Fundamentals outline comprises 5 domains. The three largest are Describe features of generative AI workloads on Azure (20-25%) (20-25%), Describe Artificial Intelligence workloads and considerations (15-20%) (15-20%), and Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) (15-20%). The full list appears in the topics section above, and the 132 practice questions at BraindumpsPrep cover all of them.

      What is the AI-901 exam, and what doors does it open?

      The AI-901 exam is a Microsoft certification exam built around the Microsoft Azure AI Fundamentals syllabus shown above. It feeds into these credential paths: Microsoft Certified: Azure AI Fundamentals. Precisely because the exam is demanding, passing it earns real attention from employers in 2026. BraindumpsPrep prepares you with 132 practice questions in PDF, Desktop Test Engine, and Online Test Engine formats.

      Are there prerequisites for the AI-901 exam?

      No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial. Requirements can change, so verify the latest on the official Microsoft exam page before scheduling.

      What do I receive with the BraindumpsPrep AI-901 package?

      The complete preparation toolkit: 132 practice questions for the Microsoft Azure AI Fundamentals exam in three formats — a printable, expert-prepared PDF with instant download; a Desktop Test Engine for Windows that simulates the real exam with two practice modes and works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. Included as well: a free demo, 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations — all from a platform refined through years of expert effort.

      Microsoft Azure AI Fundamentals Sample Questions:

      Question #1

      For each of the following statements, select Yes if the statement is true. Otherwise, select NOTE Each correct selection is worth one point.

      Reveal Solution  Discussion  0

      Correct Answer:


      Explanation:

      Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze multiple content types, including documents, forms, images, audio, and video.
      Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format. = Yes Azure Content Understanding returns structured analysis results in JSON format. This is how extracted fields, values, confidence scores, and other analysis results are represented.
      Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can use analyzers and schemas to extract structured fields from documents and forms, such as invoices, receipts, contracts, and other business documents.

      Question #2

      You have the following REST API request.

      Which Azure OpenAI model should you use to process the request?

      • A. Whisper
      • B. GPT-4.1
      • C. DALL-E
      • D. Codex
      Reveal Solution  Discussion  0

      Correct Answer: C  🗳️

      Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).

      Question #3

      You have a Microsoft Foundry project that contains a vision-enabled model deployment. You need to create a prompt that ensures the model produces a relevant and useful response. What should you include in the prompt?

      • A. the version number of the model
      • B. a dataset ID for the image
      • C. a description of the task
      • D. the deployment name of the model
      Reveal Solution  Discussion  0

      Correct Answer: C  🗳️

      Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).

      Question #4

      You have a multimodal model deployed in Microsoft Foundry
      You need to configure the model to analyze an image and provide both a written summary and an audio summary of the image.
      What is the minimum number of user prompts that should be sent to the model?

      • A. 1
      • B. 2
      • C. 3
      • D. A
      Reveal Solution  Discussion  0

      Correct Answer: A  🗳️

      Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).

      Question #5

      Select the answer that correctly completes the sentence.

      Reveal Solution  Discussion  0

      Correct Answer:


      Explanation:

      The completed sentence is:
      After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model.
      To call a deployed model from an application, the application must send requests to the deployed model's endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.
      The other options are incorrect:
      evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.
      Foundry playground is an interactive testing environment, not the production target for application requests.
      training dataset of the model is used for training or fine-tuning, not for sending inference requests.
      Therefore, the correct answer is endpoint of the model.

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