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Microsoft AI-901 : Microsoft Azure AI Fundamentals
- Exam Code: AI-901
- Exam Name: Microsoft Azure AI Fundamentals
- Updated: Oct 14, 2026 Q&As: 132 Questions and Answers
About Microsoft AI-901 Exam
Practice that scores itself teaches faster. The Microsoft Azure AI Fundamentals software version from TorrentVCE simulates the real test scene, scores your performance, and reminds you to revisit missed AI-901 questions — all 132 of them.
Microsoft AI-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe Artificial Intelligence workloads and considerations | 20-25% | - Responsible AI principles
|
| Describe features of computer vision workloads on Azure | 15-20% | - Image analysis
|
| Describe fundamental principles of machine learning on Azure | 25-30% | - Machine learning concepts
|
| Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
| Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Text analysis
|
AI-901 Exam: Your Questions, Answered
The Microsoft Azure AI Fundamentals blueprint spans 5 domains — including Describe features of computer vision workloads on Azure (15-20%), Describe fundamental principles of machine learning on Azure (25-30%), Describe Artificial Intelligence workloads and considerations (20-25%). Spend your hours where the percentages are; the full outline above lists every subtopic.
Yes:
After any official course, verify retention with the 132 practice questions for the Microsoft Azure AI Fundamentals — scored simulation shows what lectures can't.
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Yes — download the free Microsoft Azure AI Fundamentals demo and inspect real questions before paying. Your purchase then stays valid for 365 days with free updates throughout, renewable afterward at 50% off.
The Microsoft Azure AI Fundamentals is Microsoft's official exam for the Microsoft Azure AI Fundamentals certification, at the Fundamentals level. It validates practical, job-relevant skills — which is why employers shortlist certified candidates. Related credentials include Microsoft Azure Data Fundamentals (DP-900), Microsoft Azure Fundamentals (AZ-900).
Microsoft Azure AI Fundamentals Sample Questions:
Question #1
You are developing a voice application that listens for spoken commands and converts them into text 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:
The correct Python method is:
recognizer.recognize_once()
Completed code:
import azure.cognitiveservices.speech as speechsdk
speech_config = speechsdk.SpeechConfig(subscription=key, region=region) recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config) recognizer.recognize_once() Microsoft's Azure Speech documentation for Foundry Tools explains that Speech to text is used for real-time speech recognition and converting spoken audio into text. The Python Speech SDK uses a SpeechRecognizer object for recognition from audio input, such as a microphone.
Why the other options are incorrect:
recognizer.speak_text_async( " Ready " ) is incorrect because speaking text is text-to-speech, not speech-to- text recognition.
recognizer.start_continuous_recognition() can be used for continuous recognition, but the code shown is asking for the basic method to recognize spoken input and convert it to text from the SpeechRecognizer.
recognizer.start_keyword_recognition() is used for keyword/wake-word recognition, not general speech-to- text transcription of spoken commands.
Therefore, the correct answer is:
recognizer.recognize_once()
Question #2
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:
Question #3
You have a Microsoft Foundry project that contains a vision-enabled model deployment. You need to create a prompt that ensures the model produces a relevant and useful response. What should you include in the prompt?
- A. the deployment name of the model
- B. the version number of the model
- C. a description of the task
- D. a dataset ID for the image
Correct Answer: C 🗳️
Explanation: Only visible for TorrentVCE members. You can sign-up / login (it's free).
Question #4
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
The completed sentence is:
An AI workload that produces new content based on user input is an example of generative AI.
Generative AI is used to create new content, such as text, images, code, audio, or other outputs, from user prompts or instructions.
The other options are incorrect:
content understanding extracts or interprets information from existing content.
information extraction identifies and extracts structured data from content.
text analysis analyzes existing text for entities, sentiment, key phrases, language, or summaries.
Question #5
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 " )
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