A high-profile certification is hard to earn — that is precisely why it carries weight. PracticeVCE helps you prepare for the AI-900 exam the smart way: 336 expert-verified practice questions covering the Microsoft Azure AI Fundamentals objectives, current throughout 2026.
Microsoft AI-900 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Format: | Multiple select, Multiple choice, Scenario-based questions |
| Real Exam Qty: | 40–60 |
| Exam Price: | $99 USD |
| Exam Duration: | 45 minutes |
| Certificate Validity Period: | Valid indefinitely |
| Available Languages: | Russian, German, French, English, Indonesian (Indonesia), Arabic (Saudi Arabia), Spanish, Portuguese (Brazil), Chinese (Simplified), Korean, Japanese, Chinese (Traditional), Italian |
| Passing Score: | 700 (on a scale of 1–1000) |
| Related Certifications: | Microsoft Certified: Azure Fundamentals Microsoft Certified: Azure Data Fundamentals |
| Recommended Training: | Microsoft Learn: Azure AI Fundamentals Learning Path Instructor-led Training: AI-900 Course |
| Exam Registration: | Pearson VUE Registration Microsoft Certification Registration |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or onsite testing at Pearson VUE test centers |
| Pre Condition: | No required prerequisites; basic familiarity with cloud computing or AI concepts is recommended but not mandatory |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900/ |
Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe generative AI concepts |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe machine learning pipelines |
AI-900 Exam Answers Without the Headache
As of the latest information, the AI-900 exam requires a passing score of 700 (on a scale of 1–1000) and a registration fee of $99 USD. Microsoft may revise either figure, so confirm both on the official site before scheduling.
Microsoft lists these prerequisites for the Microsoft Azure AI Fundamentals: No required prerequisites; basic familiarity with cloud computing or AI concepts is recommended but not mandatory.
Check the current requirements on the official certification page before booking.
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Smart preparation means current materials, verified answers, and a schedule that fits real life. The AI-900 bank is written by a team of Microsoft experts and certified trainers, and our colleagues check it continuously for updates so accuracy never drifts. You practice in your spare time — no classes required — with expert-verified answers across the Microsoft Azure AI Fundamentals objectives. Before purchase, a free demo is downloadable for reference; after purchase, updates arrive by email for 365 days and 24/7 customer assisting handles any question. That combination is the smart way.
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Per current exam information, the AI-900 exam contains 40–60 questions within a 45 minutes-minute limit. Practicing under those exact conditions removes the format shock on exam day.
The Microsoft Azure AI Fundamentals blueprint centers on these main domains:
- Features of Natural Language Processing (NLP) workloads on Azure (15–20%)
- Features of generative AI workloads on Azure (20–25%)
- Fundamental principles of machine learning on Azure (15–20%)
The full official outline continues with further domains; the question bank covers them all.
Microsoft points candidates to these official training resources:
For office workers short on class time, disciplined spare-time practice with a current bank covers the same syllabus at a fraction of the cost.
Use the official registration channels below:
Pick your test center or online session and book ahead — a fixed date focuses preparation like nothing else.
Microsoft Azure AI Fundamentals Sample Questions:
To complete the sentence, select the appropriate option in the answer area.
Correct Answer:
Explanation:
In the context of Microsoft Azure AI Fundamentals (AI-900) and general machine learning principles, regression refers to a type of supervised learning used to predict continuous numerical values based on historical data. The goal of regression is to model the relationship between input variables (features) and a continuous output variable (target).
In this scenario, the task is to predict how many vehicles will travel across a bridge on a given day. The number of vehicles is a numerical value that can vary continuously depending on factors such as time of day, weather, weekday/weekend, or traffic trends. Because the output is numeric and not categorical, this problem type clearly fits into regression analysis.
Microsoft's official learning content for AI-900, under "Identify features of regression and classification machine learning models," specifies that regression models are used to predict values such as sales forecasts, demand estimation, temperature prediction, or traffic volume-all of which share the same underlying objective: predicting a quantity.
To clarify other options:
* Classification is used when predicting categories or discrete classes, such as determining whether an email is spam or not spam, or if an image contains a cat or a dog.
* Clustering is an unsupervised learning technique used to group similar data points without predefined labels (for example, grouping customers by purchasing behavior).
Since predicting the number of vehicles results in a continuous numerical output, it aligns precisely with the regression workload type described in the Microsoft AI-900 study materials.
You are processing photos of runners in a race.
You need to read the numbers on the runners' shirts to identity the runners in the photos.
Which type of computer vision should you use?
- A. semantic segmentation
- B. facial recognition
- C. object detection
- D. optical character recognition (OCR)
Correct Answer: D 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
Which metric can you use to evaluate a classification model?
- A. true positive rate
- B. coefficient of determination (R2)
- C. mean absolute error (MAE)
- D. root mean squared error (RMSE)
Correct Answer: A 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
Providing contextual information to improve the responses quality of a generative Al solution is an example of which prompt engineering technique?
- A. providing examples
- B. grounding data
- C. fine-tuning
- D. system messages
Correct Answer: B 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
This question refers to a system that monitors a user's emotions or expressions-in this case, identifying whether a kiosk user is annoyed-through a video feed. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify Azure services for computer vision," this scenario falls under facial analysis, which is a capability of Azure AI Vision or the Face API.
Facial analysis involves detecting human faces in images or video and analyzing facial features to interpret emotions, expressions, age, gender, or facial landmarks. The AI model does not try to identify who the person is but rather interprets how they appear or feel. For example, facial analysis can detect emotions such as happiness, anger, sadness, or surprise, which allows applications to infer a user's engagement or frustration level while interacting with a system.
Option review:
* Face detection: Identifies the presence and location of a face in an image but does not interpret expressions or emotions.
* Facial recognition: Matches a detected face to a known individual's identity (for authentication or security), not for emotion detection.
* Optical character recognition (OCR): Extracts text from images or scanned documents and has no relation to human emotion or facial features.
Therefore, determining whether a kiosk user is annoyed, happy, or frustrated involves emotion detection within facial analysis, making Facial analysis the correct answer.
This aligns with AI-900's definition of computer vision workloads, where facial analysis provides insights into emotions and expressions, supporting user experience optimization and customer behavior analytics.
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