Exam Name: Microsoft Azure AI Fundamentals
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified: Azure AI Fundamentals
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Updated: Oct 30, 2026
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- Total Questions: 336
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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 |
| Related Certifications: | Microsoft Azure AI Engineer Associate Microsoft Azure Fundamentals Microsoft Azure Data Fundamentals |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 85 minutes |
| Available Languages: | Japanese, Korean, French, Portuguese (Brazil), English, Spanish, Chinese (Simplified), German |
| Exam Price: | USD 99 (varies by region) |
| Exam Format: | Case studies, Multiple response, Multiple choice, Drag and drop |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Recommended Training: | Microsoft Learn AI-900 Learning Path Azure AI Fundamentals Practice Modules |
| Exam Registration: | Pearson VUE Exam Registration Official Microsoft Certification Page |
| Sample Questions: | DOWNLOAD DEMO |
| 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: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 3: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 4: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
What Candidates Ask About the Microsoft Azure AI Fundamentals
Expect 40-60 questions with a time limit of 85 minutes. The smart move is to treat time as a resource: allocate a rough budget per question, mark the stubborn ones and move on, then return with fresh eyes. Two or three full timed runs in the Actual4dump desktop engine — which recreates the real exam environment — will calibrate your pace far better than untimed reading ever could.
No formal prerequisites required; basic understanding of cloud and AI concepts recommended Keep in mind that vendors adjust their requirements over time, so verify the current rules on the official exam page (AI-900 official exam details) before you schedule anything.
Let's take delivery first: the instant your payment is confirmed, your download links activate and a copy is emailed to you within a minute — install on as many computers as you like, and if nothing arrives within 2 hours, our support team will sort it out. Now the safety net: sit the corresponding AI-900 exam within 60 days of purchase and, if you don't pass, claim a full refund under our 100% Money Back Guarantee by submitting a scanned enrollment slip and the official Score Report PDF within 2 days of the exam; processing finishes within 7 days. Conditions apply: exams taken within 3 days of purchase are excluded, the candidate's name must match the payer's, and free or expired products aren't eligible. If you'd rather exchange than refund, we'll give you two other exam products of equal value for free, and your original updates keep running.
Per the official blueprint, the Microsoft Azure AI Fundamentals is organized into 4 domains — among them Describe AI workloads and considerations (20-25%), Fundamentals of machine learning on Azure (25-30%), Features of computer vision workloads on Azure (15-20%). The weights show where the exam spends its questions, so let them guide your study hours. The full domain-and-subtopic breakdown is in the outline section above.
Booking happens through the vendor's official registration channels:
Depending on your preference, the AI-900 exam is offered Online proctored exam or in-person test center — select the option that suits you during registration.
Yes. Try the free PDF demo first — it shows you the exact question style and answer quality before you spend anything. When you buy, 365 days of free updates come standard, with the newest versions sent straight to your mailbox; if your update period lapses later, you can renew it at 50% off in your member zone.
The vendor recommends these official training options:
Training covers the theory well, but self-assessment is what catches weak spots. Once the coursework is done, run the AI-900 practice questions from Actual4dump and let your scores reveal what still needs work.
The official fee for the AI-900 exam is USD 99 (varies by region), and passing requires 700/1000. Remember that the fee buys exactly one attempt — a retake costs the same amount again. That is a good reason to rehearse with the 336 practice questions from Actual4dump until you're consistently scoring past the threshold before paying for the real thing.
The Microsoft Azure AI Fundamentals is the vendor's official exam for the Microsoft Certified: Azure AI Fundamentals certification, a credential at the Fundamentals level. It is widely recognized because it verifies genuine, job-ready skill — exactly what employers shortlist for. It also connects to related credentials including Microsoft Azure Data Fundamentals, Microsoft Azure Fundamentals, Microsoft Azure AI Engineer Associate, which makes it a solid anchor for a longer certification roadmap.
Microsoft Azure AI Fundamentals Sample Questions:
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 website that includes customer reviews.
You need to store the reviews in English and present the reviews to users in their respective language by recognizing each user's geographical location.
Which type of natural language processing workload should you use?
- A. speech recognition
- B. translation
- C. language modeling
- D. key phrase extraction
Correct Answer: B 🗳️
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Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision capabilities refer to artificial intelligence systems that can analyze and interpret visual content such as images and videos. The Azure AI Vision and Face API services provide pretrained models for detecting, recognizing, and analyzing visual information, enabling developers to build intelligent applications that understand what they " see. " When asked how computer vision capabilities can be deployed, the correct answer is to integrate a face detection feature into an app. This aligns with Microsoft Learn's module "Describe features of computer vision workloads," which explains that computer vision can identify objects, classify images, detect faces, and extract text (OCR). The Face API, a part of Azure AI Vision, specifically provides face detection, verification, and emotion recognition capabilities.
Integrating these services into an application allows it to perform actions such as:
* Detecting human faces in photos or video streams.
* Recognizing facial attributes like age, emotion, or head pose.
* Enabling secure authentication based on face recognition.
The other options are incorrect because they relate to different AI workloads:
* Develop a text-based chatbot for a website: This falls under Conversational AI, implemented with Azure Bot Service or Conversational Language Understanding (CLU).
* Identify anomalous customer behavior on an online store: This task relates to machine learning and anomaly detection models, not computer vision.
* Suggest automated responses to incoming email: This uses Natural Language Processing (NLP) capabilities, not visual analysis.
Therefore, the correct and Microsoft-verified completion of the statement is:
"Computer vision capabilities can be deployed to integrate a face detection feature into an app."
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 "Describe features of conversational AI workloads," the correct answer is Power Virtual Agents. This service is part of the Microsoft Power Platform and is specifically designed to enable users to create intelligent chatbots without writing any code.
Power Virtual Agents (PVA) provides a no-code/low-code environment where business users and developers can collaboratively design conversational experiences. It integrates built-in natural language processing (NLP) models to understand user intent and respond intelligently to text or speech inputs. The platform's interface allows chatbot creators to design dialogues visually, connect to back-end data via Power Automate, and publish bots on websites, Teams, or other communication channels.
This approach is highlighted in Microsoft Learn as an ideal solution for organizations that want to deploy conversational bots quickly without requiring specialized AI or programming expertise. PVA automatically leverages Microsoft's language understanding models, allowing it to interpret user input and map it to predefined topics or actions.
Let's analyze the other options:
* Azure Health Bot: A specialized solution for the healthcare industry that provides prebuilt medical compliance and healthcare content. It is not a general-purpose, no-code chatbot builder.
* Microsoft Bot Framework: A developer-focused SDK for building highly customized bots through code, offering maximum flexibility but not no-code functionality.
Therefore, the most appropriate choice that "can be used to build no-code apps that use built-in natural language processing models" is Power Virtual Agents - the official Microsoft no-code chatbot solution for conversational AI workloads.
You have a dataset that contains information about taxi journeys that occurred during a given period.
You need to train a model to predict the fare of a taxi journey.
What should you use as a feature?
- A. the trip distance of individual taxi journeys
- B. the number of taxi journeys in the dataset
- C. the fare of individual taxi journeys
- D. the trip ID of individual taxi journeys
Correct Answer: A 🗳️
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