最實用的AI-901認證考試的學習資料

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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement AI solutions by using Microsoft Foundry55-60%- Work with models in Microsoft Foundry
  • 1. Evaluate model performance and safety
  • 2. Customize models with prompt engineering
  • 3. Fine-tune models
  • 4. Discover and deploy foundation models
- Implement AI solutions
  • 1. Use code examples to call AI services
  • 2. Implement single-agent solutions
  • 3. Integrate AI services into applications
  • 4. Monitor and manage AI solutions
- Get started with Microsoft Foundry
  • 1. Navigate the Microsoft Foundry portal
  • 2. Describe core features and use cases of Microsoft Foundry
  • 3. Create and configure a Foundry project
Topic 2: Identify AI concepts and responsibilities40-45%- Identify AI model components and configurations
  • 1. Describe data preparation for AI models
  • 2. Describe model training and evaluation concepts
  • 3. Describe model deployment and consumption concepts
- Identify AI concepts and features
  • 1. Describe capabilities of computer vision
  • 2. Describe capabilities of natural language processing
  • 3. Describe characteristics of generative AI
  • 4. Describe capabilities of machine learning
  • 5. Describe types of AI workloads
- Describe principles of responsible AI
  • 1. Describe reliability and safety considerations
  • 2. Describe transparency considerations
  • 3. Describe privacy and security considerations
  • 4. Describe inclusiveness considerations
  • 5. Describe fairness considerations
  • 6. Describe accountability considerations

>> AI-901認證資料 <<

AI-901認證資料 - 成功通過Microsoft Azure AI Fundamentals的利刃

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最新的 Microsoft Certified: Azure AI Fundamentals AI-901 免費考試真題 (Q35-Q40):

問題 #35
Hotspot Question
Select the answer that correctly complete the sentence.

答案：

解題說明：


問題 #36
You are developing an application that continuously transcribes speech from a default microphone by using Azure Speech in Microsoft Foundry Tools.
You need to perform speech-to-text transcription from the microphone.
Which Python class should you use?

答案：C

解題說明：
speechsdk.SpeechRecognizer is the appropriate Python class to handle continuous real-time speech-to-text transcription from a default microphone.
How to Implement It
To achieve continuous transcription (rather than transcribing a single sentence and stopping), you instantiate this class and invoke its asynchronous long-running method.speechsdk.SpeechRecognizer: Acts as the core proxy client connecting your application to the Azure Speech service.
start_continuous_recognition(): The essential method on this class that instructs the SDK to stream microphone data indefinitely and process multi-sentence conversations.
Reference:
https://stackoverflow.com/questions/67691338/continuous-speech-recognition-from-microphone-on-ms-azure


問題 #37
Hotspot Question
Select the answer that correctly completes the sentence.

答案：

解題說明：

Explanation:
Box: IProjectClient
The AIProjectClient class (from the azure-ai-projects package) is the primary entry point used to access project deployments, agents, and indexes when building applications with the Microsoft Foundry SDK.
Reference:
https://pypi.org/project/azure-ai-projects/


問題 #38
Based on the image provided, here is the transcribed text:
You need to build an AI solution that produces new product images based on written descriptions provided by users.
Which AI workload should you use?

答案：B

解題說明：
The requirement is to produce new product images based on written descriptions. This is an image generation workload, because the AI system is creating entirely new images from natural language prompts.


問題 #39
Hotspot Question
Select the answer that correctly completes the sentence.

答案：

解題說明：

Explanation:
Box: Computer vision
Computer vision uses Optical Character Recognition (OCR) and deep learning models to detect, localize, and extract printed or handwritten text from images, turning visual pixels into editable digital data.
Reference:
https://docs.cloud.google.com/vision/docs/ocr


問題 #40
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