- Exam Code: AI-900
- Exam Name: Microsoft Azure AI Fundamentals
- Updated: Oct 15, 2026
- Q & A: 336 Questions and Answers
Microsoft AI-900 actual dump : Microsoft Azure AI Fundamentals
PDF Version DemoAbout Microsoft AI-900 Exam Questions
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Microsoft AI-900 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Related Certifications: | Microsoft Azure Fundamentals Microsoft Azure AI Engineer Associate Microsoft Azure Data Fundamentals |
| Exam Duration: | 85 minutes |
| Exam Format: | Multiple choice, Multiple response, Drag and drop, Case studies |
| Exam Price: | USD 99 (varies by region) |
| Available Languages: | French, English, Korean, Chinese (Simplified), Spanish, Portuguese (Brazil), German, Japanese |
| Real Exam Qty: | 40-60 |
| Recommended Training: | Microsoft Learn AI-900 Learning Path Azure AI Fundamentals Practice Modules |
| Exam Registration: | Official Microsoft Certification Page Pearson VUE Exam Registration |
| Sample Questions: |  |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required; basic understanding of cloud and AI concepts recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/ |
Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 2: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 3: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 4: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
Everything You Want to Know About the Microsoft Azure AI Fundamentals (AI-900) Exam
The AI-900 exam, officially titled Microsoft Azure AI Fundamentals, is the Microsoft exam you pass to earn the Microsoft Certified: Azure AI Fundamentals certification, a credential at the Fundamentals level. Passing it confirms the skills defined in the official exam outline, and it is associated with related credentials such as Microsoft Azure Data Fundamentals, Microsoft Azure Fundamentals, Microsoft Azure AI Engineer Associate.
The AI-900 exam includes 40-60 questions and gives you 85 minutes to finish them. That is a tight pace per question, so get used to reading each stem once, flagging anything uncertain, and moving on rather than getting stuck. Running timed practice tests in the Actual4Dumps desktop or online test engine is the most reliable way to build that rhythm before exam day.
The passing score for the AI-900 exam is 700/1000, and the official registration fee is USD 99 (varies by region). Keep in mind that a failed attempt means paying USD 99 (varies by region) again in full to retake it, so book your slot only when your scores on Actual4Dumps practice tests sit consistently above the passing line.
Microsoft lists the following prerequisites or eligibility notes for the AI-900 exam: No formal prerequisites required; basic understanding of cloud and AI concepts recommended. Requirements can change over time, so confirm the details on the official exam page at Microsoft's official site before you register.
You can register for the AI-900 exam through the official channels below:
The exam is delivered in the following format: Online proctored exam or in-person test center. Choose a date that leaves you enough time to work through the full 336-question practice set first.
Microsoft recommends the following training resources for AI-900 candidates:
Official courses build the theory, and the 336 practice questions from Actual4Dumps help you turn that theory into exam-ready speed and accuracy.
Yes. Actual4Dumps offers a free PDF demo of the AI-900 material, so you can check the question style and answer quality before purchasing. Every purchase also includes 365 days of free updates, and if your product expires you can extend the update service at a 50% discount from your member zone.
Your purchase is protected by a 100% Money Back Guarantee with clear conditions: if you take the corresponding AI-900 exam within 60 days of purchase and do not pass, you can apply for a full refund. The candidate name must match the payer name, and you need to submit a scanned exam enrollment slip together with the official Score Report PDF within 2 days of taking the exam; claims are processed within 7 days. Sitting the exam within 3 days of purchase, downloading without taking the exam, free materials, and expired orders are not covered. If you would rather not take a refund, you can exchange your order for two free exam products of equal value and keep the update service on your original purchase.
Delivery is instant: your download links are emailed within one minute of payment, and you can also download directly from the website. If nothing arrives within 2 hours, contact customer service and check your spam folder. There is no limit on how many computers you can install the material on.
The AI-900 exam blueprint is divided into 4 domains, starting with Features of computer vision workloads on Azure (15-20%); Fundamentals of machine learning on Azure (25-30%); Features of natural language processing (NLP) workloads on Azure (30-35%). For the complete domain-by-domain breakdown, see the Exam Topics section above — it lists every topic the current outline covers.
Microsoft Azure AI Fundamentals Sample Questions:
Question #1
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:
This question tests understanding of Microsoft's six guiding principles for Responsible AI, which are:
fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles, as described in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Responsible AI module, help ensure that AI systems are developed and used ethically and responsibly.
* Transparency - Yes:Transparency means users should understand how and why an AI system makes certain decisions. Providing an explanation of the outcome of a credit loan application clearly supports transparency because it helps customers know the reasoning behind approval or rejection. According to Microsoft Learn, transparency ensures that "AI systems are understandable by users and stakeholders," especially in sensitive applications such as finance and credit scoring. Thus, the first statement is Yes.
* Reliability and Safety - Yes:The reliability and safety principle ensures AI systems perform consistently, safely, and as intended, even in complex or high-risk environments. A triage bot that prioritizes insurance claims based on injury type aligns with this principle-it must be accurate, dependable, and safe to ensure claims are processed correctly and not influenced by errors or faulty algorithms. Microsoft teaches that AI should be "reliable under expected and unexpected conditions" to prevent harm or misjudgment. Therefore, this statement is Yes.
* Inclusiveness - No:Inclusiveness focuses on ensuring AI systems empower and benefit all users, especially those with different abilities or backgrounds. Offering an AI solution at different prices across sales territories is a business decision, not an ethical or inclusiveness principle issue. It does not relate to accessibility or equal participation of diverse users. Therefore, this final statement is No.
Question #2
You need to develop a mobile app for employees to scan and store their expenses while travelling.
Which type of computer vision should you use?
- A. image classification
- B. semantic segmentation
- C. optical character recognition (OCR)
- D. object detection
Correct Answer: C 🗳️
Explanation: Only visible for Actual4Dumps members. You can sign-up / login (it's free).
Question #3
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?
- A. anomaly detection
- B. natural language processing
- C. conversational AI
- D. computer vision
Correct Answer: D 🗳️
Explanation: Only visible for Actual4Dumps members. You can sign-up / login (it's free).
Question #4
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using Azure Al Language Service ' s question answering? Each correct answer presents a complete solution.
NOTE: Each correct and ask questions by selection is worth one point.
- A. Connect the bot to the Cortana channel using Cortana.
- B. Manually enter the questions and answers.
- C. Import chit-chat content from a predefined data source.
- D. Use Azure Machine Learning Automated ML to train a model based on a file that contains question and answer pairs.
- E. Generate the questions and answers from an existing webpage.
Correct Answer: B,C,E 🗳️
Explanation: Only visible for Actual4Dumps members. You can sign-up / login (it's free).
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:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Azure Machine Learning documentation, Automated Machine Learning (AutoML) is a feature designed to help users build, train, and tune machine learning models automatically without requiring deep knowledge of programming or data science.
* First Statement: "Automated machine learning provides you with the ability to include custom Python scripts in a training pipeline."This is False (No). AutoML automates the model selection and tuning process but does not allow the inclusion of custom Python scripts within its workflow. Custom Python integration is supported in Azure Machine Learning designer pipelines or SDK-based training, not in AutoML.
* Second Statement: "Automated machine learning implements machine learning solutions without the need for programming experience."This is True (Yes). One of AutoML's core benefits is that it enables non-programmers to train and evaluate models by simply selecting data, choosing a target column, and letting Azure automatically test algorithms and hyperparameters. This aligns with Microsoft's AI-900 objective to democratize AI development.
* Third Statement: "Automated machine learning provides you with the ability to visually connect datasets and modules on an interactive canvas."This is False (No). That feature belongs to Azure Machine Learning Designer, not AutoML. The designer offers a drag-and-drop visual interface for connecting datasets and modules, whereas AutoML provides a wizard-driven approach focused on automation.
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