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Microsoft AI-901 Exam Overview:
| Certification Vendor: | Microsoft |
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| Exam Name: | Microsoft Azure AI Fundamentals |
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| Exam Number: | AI-901 |
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| Exam Duration: | 60 minutes |
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| Passing Score: | 700 |
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| Exam Format: | Multiple choice, Multiple select, Case studies |
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| Real Exam Qty: | 30-40 |
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| Available Languages: | Russian, Indonesian (Indonesia), Chinese (Traditional), Japanese, Korean, French, Chinese (Simplified), Portuguese (Brazil), Italian, Spanish, English, German, Arabic (Saudi Arabia) |
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| Exam Price: | $99 USD |
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| Certificate Validity Period: | Lifetime |
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| Recommended Training: | Microsoft Learn: AI concepts for developers and technology professionals Microsoft Learn: Get started with Microsoft Foundry |
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| Exam Registration: | Pearson VUE Scheduling Microsoft Certification Exam Registration |
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| Sample Questions: | Microsoft AI-901 Sample Questions |
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| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
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| Pre Condition: | No required prerequisites; basic understanding of cloud computing and familiarity with Python syntax recommended |
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| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-901/ |
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| Topic | Details |
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| 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.
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| 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.
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Microsoft Azure AI Fundamentals Sample Questions (Q104-Q109):
NEW QUESTION # 104
What should you use to identify similar faces in a set of images?
- A. Azure AI Vision
- B. Azure AI Language
- C. Azure AI Custom Vision
- D. Azure OpenAI Service
Answer: A
Explanation:
To identify similar faces across different images using Azure, the Azure AI Face service is the most suitable choice. This service leverages AI algorithms to detect, recognize, and analyze human faces, including features like face grouping based on visual similarity.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/find-similar-faces
NEW QUESTION # 105
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:

Statement 1: An Azure Content Understanding in Foundry Tools analyzer returns results in the HTML format. = No Azure Content Understanding returns analysis results as structured output, typically JSON , not HTML.
Microsoft documentation states that the Content Understanding API returns analysis results in a structured JSON format.
Statement 2: The Content Understanding REST API analyzes content synchronously and returns results in the same response. = No Content Understanding analysis is asynchronous. The quickstart states that the response header includes an Operation-Location field, which is used to retrieve the results of the asynchronous analysis operation.
Statement 3: Azure Content Understanding in Foundry Tools can transform documents, images, audio, and video files into structured output. = Yes Microsoft describes Azure AI Content Understanding as processing documents, images, videos, and audio, and transforming them into user-defined output formats.
NEW QUESTION # 106
Select the answer that correctly completes the sentence.

Answer:
Explanation:


NEW QUESTION # 107
Select the answer that correctly completes the sentence.

Answer:
Explanation:

Explanation:
The correct answer is Creating captions for a video recording . Speech recognition, also called speech-to-text , converts spoken audio into textual output. Microsoft explicitly defines Azure Speech-to-Text as speech recognition technology that transcribes audio streams or prerecorded audio into text.
Caption generation is a direct application of this capability. Microsoft describes captioning as converting the audio content of a video, film, webcast, or other production into text and displaying that text visually.
Therefore, creating captions from the spoken content of a video is a canonical speech-recognition workload.
Creating an audio commentary is instead associated with speech synthesis or text-to-speech because the required output is spoken audio. Identifying key phrases in a video transcript is a text-analysis/NLP task performed after the speech has already been transcribed. A voice-activated security system may involve voice authentication, speaker recognition, or command recognition, but it is not as unambiguously a speech-to-text workload as caption generation.
The AI-901 Study Guide specifically requires candidates to distinguish the features and capabilities of speech recognition and speech synthesis .
NEW QUESTION # 108
You plan to create an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. The solution will be billed via tokens for inputs and outputs to the API. Which deployment option should you use?
- A. Azure Kubernetes Service (AKS) cluster
- B. Azure virtual machines
- C. managed compute
- D. serverless API
Answer: D
Explanation:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/deployments-overview
NEW QUESTION # 109
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