Not everyone has evenings free for a bootcamp. If your AI-901 preparation has to happen on the subway, in waiting rooms, or after the kids are asleep, the 127 Microsoft Azure AI Fundamentals practice questions from Actual4Exams were made for that kind of schedule in 2026.
Microsoft AI-901 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Passing Score: | 700 / 1000 |
| Available Languages: | Indonesian (Indonesia), Russian, Korean, German, French, Arabic (Saudi Arabia), Chinese (Traditional), Japanese, Chinese (Simplified), Portuguese (Brazil), Italian, Spanish, English |
| Exam Format: | Match the service to use case, Scenario-based, Drag and drop, Multiple select, Multiple choice |
| Exam Duration: | 45-60 |
| Exam Price: | USD 99 |
| Real Exam Qty: | 40-60 |
| Sample Questions: |  |
| Exam Way: | Online (remote proctored via Pearson VUE) or at a Pearson VUE test center |
| Pre Condition: | 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. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900 |
Microsoft AI-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of computer vision workloads on Azure (15-20%) | 15-20% | - Identify Azure tools and services for computer vision tasks
|
| Topic 2: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) | 15-20% | - Identify Azure tools and services for NLP workloads
|
| Topic 3: Describe Artificial Intelligence workloads and considerations (15-20%) | 15-20% | - Identify guiding principles for responsible AI
|
| Topic 4: Describe features of generative AI workloads on Azure (20-25%) | 20-25% | - Identify generative AI services and capabilities in Microsoft Azure
|
| Topic 5: Describe fundamental principles of machine learning on Azure (15-20%) | 15-20% | - Describe core machine learning concepts
|
FAQ: Preparing for Microsoft Azure AI Fundamentals the Smart Way
Microsoft Azure AI Fundamentals is an official exam run by Microsoft under exam code AI-901. Passing it awards the Microsoft Certified: Azure AI Fundamentals certification, which sits at the Fundamental tier. It also counts toward related credentials such as Microsoft Certified: Azure AI Fundamentals. Certified professionals remain in shorter supply than the market wants, which is precisely why this exam keeps showing up in conversations about better roles and better pay.
The Microsoft Azure AI Fundamentals exam gives you 45-60 to work through 40-60 questions. That is a tight ratio, and it punishes candidates who get emotionally attached to any single item. The fix is mechanical: answer what you know, flag what you do not, and keep moving. A few full-length timed runs in the Actual4Exams test engine, with its randomized question order, will calibrate your pace far better than untimed reading ever could.
The official fee for Microsoft Azure AI Fundamentals is USD 99, and 700 / 1000 is what passing takes. The uncomfortable part: retakes cost the full USD 99 again, which makes preparation the cheapest line item in this whole project. Before booking, put yourself through repeated scored sessions with the Actual4Exams practice tests and compare results over time; a stable margin above the passing line, not a single lucky run, is when you are ready.
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.
Vendor rules do get revised, so treat this as your starting point and confirm the current eligibility details before booking via the official exam page.
It is. Actual4Exams publishes a free PDF demo of the Microsoft Azure AI Fundamentals material, so the product can prove itself before you pay. Your purchase then comes with 365 days of free updates, and once that period ends, extending the update service costs 50% of the regular price. The test engine software itself is verified malware-free and safe to install.
Actual4Exams stands behind the product with a 100% money-back guarantee under defined conditions. If you take the Microsoft Azure AI Fundamentals exam within 60 days of purchase and fail, you qualify for a full refund, provided the exam corresponds to your product. Sitting the exam within 3 days of purchase does not qualify, and neither do unused downloads, free materials, or expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are resolved within 7 days. You may also choose an exchange instead of a refund: two other exam products of equal value, free, with the update service on your original purchase retained.
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Microsoft Azure AI Fundamentals breaks down into 5 official domains, led by Describe fundamental principles of machine learning on Azure (15-20%) (15-20%), Describe features of generative AI workloads on Azure (20-25%) (20-25%), and Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) (15-20%). You will find the full topic-by-topic outline above on this page; use the weightings to budget your study hours where they pay back the most.
Microsoft Azure AI Fundamentals Sample Questions:
You are developing an application that converts text into spoken audio and saves the synthesized audio to a file by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
AudioOutputConfig(filename= " output.wav " )
The question specifically states the application must save the synthesized audio to a file . In the Azure Speech SDK for Python, speechsdk.audio.AudioOutputConfig(filename= " output.wav " ) directs the synthesizer to write the generated speech output directly to a WAV file on disk - which is exactly the requirement.
Why the other options are wrong:
* AudioOutputConfig(stream) - This routes audio output to an in-memory audio stream object, not a file. It is used when you want to process or play the audio programmatically without saving it to disk.
* AudioStreamFormat(wave_stream_format=AudioStreamWaveFormat.PCM) - This class defines the format of an audio stream (e.g., PCM encoding, sample rate). It is used when configuring custom audio streams, not when specifying a file output destination. It is not a valid argument for speechsdk.audio. in this context.
The correct and complete line is:
audio_config = speechsdk.audio.AudioOutputConfig(filename= " output.wav " )
Select the answer that correct completes the sentence.
Correct Answer:
Explanation:
Azure Text Analytics client library
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis features include sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics capabilities. These are accessed by using the Azure Text Analytics client library .
The highlighted Azure Speech SDK option is incorrect because it is used for speech workloads, such as speech recognition and speech synthesis, not text analysis.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
The completed sentence is:
To define an agent ' s role and behaviors, you must configure a system prompt for the agent.
In Microsoft Foundry / Azure AI agent scenarios, the system prompt , also referred to as system instructions
, defines how the agent should behave, what role it should follow, and what constraints it must observe.
The other options are incorrect:
deployment slot relates to hosting/deployment routing, not agent behavior.
embedding index is used for vector search or retrieval scenarios, not defining the agent's role.
fine tuning job changes or adapts model behavior through training, but it is not the standard configuration used to define an agent's role and behavior at runtime.
Therefore, the correct answer is system prompt .
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:
Statement 1: Human-in-the-loop practices provide accountability for AI-generated decisions. = Yes Microsoft responsible AI guidance recommends keeping a human in the loop, maintaining human oversight, and ensuring humans have a role in decisions based on model output.
Statement 2: Deploying an AI system to a production environment eliminates the need for ongoing monitoring. = No Microsoft guidance states that after deployment, an AI-powered product or feature requires ongoing monitoring and improvement .
Statement 3: Disclosing the team that designed and deployed an AI system provides accountability for the system's output. = Yes Accountability in responsible AI means people and organizations remain responsible for AI systems and their effects. Identifying the people or team responsible for designing and deploying the system supports accountability and governance.
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
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