Everything you need for Microsoft Azure AI Fundamentals preparation lives in one place at Pass4suresVCE: a printable PDF, two test engines, free updates for 365 days, and a demo to try first. Grab the AI-900 package in 2026 and turn a vague study plan into a finished one.
Microsoft AI-900 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Duration: | 45-60 |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | No expiration (certification does not expire) |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Real Exam Qty: | 40-60 |
| Exam Format: | Single-choice, Drag and drop, Multiple-choice, Sequence ordering, True/False |
| Exam Price: | USD 99.00 |
| Available Languages: | Spanish, Japanese, German, English, French, Chinese (Simplified), Korean, Indonesian, Portuguese (Brazil) |
| Sample Questions: |  |
| Exam Way: | Online proctored exam or in-person testing center (Pearson VUE) |
| Pre Condition: | No formal prerequisites required. Basic knowledge of cloud computing concepts is recommended but not mandatory. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/certifications/exams/ai-900/ |
Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify common NLP tasks - Identify Azure AI services for NLP |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML |
| Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities - Describe generative AI concepts |
| Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
AI-900 Exam FAQ: What Candidates Ask About Microsoft Azure AI Fundamentals
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 Foundational level. Passing it confirms the skills defined in the official exam outline, and it is associated with related credentials such as Microsoft Certified: Azure AI Fundamentals.
The AI-900 exam includes 40-60 questions and gives you 45-60 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 Pass4suresVCE 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 99.00. Keep in mind that a failed attempt means paying USD 99.00 again in full to retake it, so book your slot only when your scores on Pass4suresVCE 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 knowledge of cloud computing concepts is recommended but not mandatory.. Requirements can change over time, so confirm the details on the official exam page at Microsoft's official site before you register.
Yes. Pass4suresVCE 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 5 domains, starting with Describe features of Generative AI workloads on Azure (15-20%); Describe features of computer vision workloads on Azure (15-20%); Describe fundamental principles of machine learning 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:
You are building a tool that will process images from retail stores and identify the products of competitors.
The solution will use a custom model.
Which Azure Cognitive Services service should you use?
- A. Custom Vision
- B. Form Recognizer
- C. Face
- D. Computer Vision
Correct Answer: A 🗳️
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
To complete the sentence, select the appropriate option in the answer area.
Correct Answer:
Explanation:
According to Microsoft's Responsible AI principles, one of the key guiding values is Reliability and Safety, which ensures that AI systems operate consistently, accurately, and safely under all intended conditions. The AI-900 study materials and Microsoft Learn modules explain that an AI system must be trustworthy and dependable, meaning it should not produce results when the input data is incomplete, corrupted, or significantly outside the expected range.
In the given scenario, the AI system avoids providing predictions when important fields contain unusual or missing values. This behavior demonstrates reliability and safety because it prevents the system from making unreliable or potentially harmful decisions based on bad or insufficient data. Microsoft emphasizes that AI systems must undergo extensive validation, testing, and monitoring to ensure stable performance and predictable outcomes, even when data conditions vary.
The other options do not fit this scenario:
* Inclusiveness ensures that AI systems are accessible to and usable by all people, regardless of abilities or backgrounds.
* Privacy and Security focuses on protecting user data and ensuring it is used responsibly.
* Transparency involves making AI decisions explainable and understandable to humans.
Only Reliability and Safety directly address the concept of an AI system refusing to act or returning an error when it cannot make a trustworthy prediction. This principle helps prevent inaccurate or unsafe outputs, maintaining confidence in the system's integrity.
Therefore, ensuring an AI system does not produce predictions when input data is incomplete or unusual aligns directly with Microsoft's Reliability and Safety principle for responsible AI.
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. optical character recognition (OCR)
- B. semantic segmentation
- C. image classification
- D. object detection
Correct Answer: A 🗳️
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning," a regression model is used when the goal is to predict a continuous numerical value based on historical data.
In this question, the task is to predict the sale price of auctioned items, which is a numeric output that can take on a wide range of values (for example, $50.25, $199.99, etc.). This makes it a regression problem because the output is continuous rather than categorical.
Regression models analyze the relationship between input features (such as item type, condition, age, bidding history, or demand) and a numerical target variable (the sale price). Common regression algorithms include linear regression, decision tree regression, and neural network regression. In Azure Machine Learning, these models are trained using labeled datasets containing known outcomes to learn patterns and make future predictions.
Let's review the incorrect options:
* Classification: Used to predict discrete categories or labels, such as "sold" vs. "unsold" or "low,"
"medium," "high." It cannot output continuous numeric predictions.
* Clustering: An unsupervised technique used to group similar data points based on shared characteristics, not to predict specific numeric outcomes.
Therefore, because predicting a sale price involves forecasting a continuous numerical value, the correct model type is Regression.
This aligns with Microsoft's AI-900 teaching that regression is used for tasks such as:
* Predicting house prices
* Forecasting sales revenue
* Estimating car values or auction prices
You have a dataset that contains the columns shown in the following table.
You have a machine learning model that predicts the value of ColumnE based on the other numeric columns.
Which type of model is this?
- A. clustering
- B. regression
- C. analysis
Correct Answer: B 🗳️
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