Free PDF 2026 AI-901: Professional Microsoft Azure AI Fundamentals Hot Questions

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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-900
Available Languages:Chinese (Simplified), Japanese, German, Spanish, Indonesian (Indonesia), Chinese (Traditional), Portuguese (Brazil), French, Russian, Korean, Italian, Arabic (Saudi Arabia), English
Certificate Validity Period:1 year (renewable via free online assessment on Microsoft Learn)
Passing Score:700 / 1000
Exam Format:Multiple select, Drag and drop, Match the service to use case, Multiple choice, Scenario-based
Exam Price:USD 99
Real Exam Qty:40-60
Related Certifications:Microsoft Certified: Azure AI Fundamentals
Exam Duration:45-60
Sample Questions:Microsoft AI-901 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

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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.
Topic 2
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.

Microsoft Azure AI Fundamentals Sample Questions (Q112-Q117):

NEW QUESTION # 112
You have the following REST API request.

Which Azure OpenAI model should you use to process the request?

Answer: D

Explanation:
The REST API request contains an image-generation style payload:
{
" prompt " : " A polar bear on the beach holding an icecream " ,
" size " : " 1024x1024 " ,
" n " : 1,
" style " : " vivid "
}
This request asks the model to generate an image from a text prompt. In Azure OpenAI, DALL-E models are used for image generation from natural language prompts.
Why the other options are incorrect:
B). Codex is associated with code generation tasks.
C). GPT-4.1 is primarily used for text/chat and multimodal reasoning, not this classic image-generation API payload.
D). Whisper is used for speech-to-text transcription.


NEW QUESTION # 113
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.
Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.
The other options are incorrect:
diarization identifies or separates speakers, such as "Speaker 1" and "Speaker 2." key phrase extraction identifies important phrases from text after text exists.
synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.


NEW QUESTION # 114
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 115
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?

Answer: B

Explanation:
To compare the costs, capabilities, and performance of large language models (LLMs) in the Foundry portal, you should use the Model leaderboard.
Why Use the Model Leaderboard
The leaderboard allows you to:
Compare models side-by-side: Evaluate multiple LLMs simultaneously for trade-offs regarding estimated costs, throughput, safety, and quality.
Make informed decisions: Select the most cost-effective model for specific workloads before committing to deployments, reducing the risk of expensive errors or overspending.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/manage-costs


NEW QUESTION # 116
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final output is an example of the Microsoft responsible AI principle of accountability .
Accountability means people and organizations remain responsible for AI systems and their effects. Human review and responsibility for final decisions are examples of accountability.
The other options are incorrect:
fairness focuses on avoiding bias and treating people fairly.
privacy and security focuses on protecting data and restricting access.
transparency focuses on explaining AI use, capabilities, and limitations.


NEW QUESTION # 117
......

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