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Microsoft AI-901 Exam Syllabus Topics:
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Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901
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Microsoft Azure AI Fundamentals Sample Questions:
Which action can be performed by using the Azure Vision in Foundry Tools service?
- A. creating thumbnails for training videos
- B. extracting key phrases from documents
- C. identifying breeds of animals in live video streams
- D. extracting data from handwritten letters
Correct Answer: D π³οΈ
Explanation: Only visible for Pass4sures members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:

Explanation:
The correct selection is AIProjectClient. In the Microsoft Foundry SDK, AIProjectClient serves as the primary project-level client for accessing and managing resources associated with a Foundry project.
Microsoft documentation specifically exposes operation groups through this client for deployments, agents, and indexes, which directly matches the requirements stated in the question.
For example, the SDK exposes deployments operations for working with models deployed to the project, agents operations for creating and managing agents, and indexes operations for accessing project search indexes. Microsoft also describes the Azure AI Projects client library as a unified Foundry SDK component that connects applications to project resources through a single project endpoint.
ChatCompletionsClient is focused primarily on model inference and chat-completion operations rather than comprehensive project-resource management. FoundryLocalManager is associated with local Foundry capabilities, while ModelCatalogClient would relate to model discovery/catalog functionality rather than deployments, agents, and indexes collectively.
Therefore, the class required for this scenario is AIProjectClient.
Select the answer that correctly completes the sentence.
Correct Answer:

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.
You are developing an application that analyzes uploaded video files by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application extracts structured information from the videos.
What should the application call?
- A. the Analyze API in Content Understanding
- B. the Image Analysis API in Azure Vision in Foundry Tools
- C. Azure Language in Foundry Tools sentiment analysis
- D. Azure OpenAI
Correct Answer: A π³οΈ
Select the answer that correctly completes the sentence.
Correct Answer:

Explanation:
Counting the number of animals in an area based on a video feed is an example of computer vision.
Computer vision is the AI workload used to analyze visual content such as images and video. Counting animals in a video feed requires detecting visual objects in video frames and analyzing them, which is a computer vision task.
Forecasting predicts future values or trends from historical data.
Knowledge mining extracts insights from large document or data collections.
Therefore, the correct answer is computer vision.
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