AI-901 Microsoft Azure AI Fundamentals neueste Studie Torrent & AI-901 tatsächliche prep Prüfung

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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Identify AI concepts and responsibilities40-45%- Identify AI concepts and features
  • 1. Describe capabilities of computer vision
  • 2. Describe capabilities of natural language processing
  • 3. Describe types of AI workloads
  • 4. Describe capabilities of machine learning
  • 5. Describe characteristics of generative AI
- Describe principles of responsible AI
  • 1. Describe reliability and safety considerations
  • 2. Describe accountability considerations
  • 3. Describe privacy and security considerations
  • 4. Describe fairness considerations
  • 5. Describe inclusiveness considerations
  • 6. Describe transparency considerations
- 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
Topic 2: Implement AI solutions by using Microsoft Foundry55-60%- 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
- Implement AI solutions
  • 1. Use code examples to call AI services
  • 2. Monitor and manage AI solutions
  • 3. Implement single-agent solutions
  • 4. Integrate AI services into applications
- Work with models in Microsoft Foundry
  • 1. Customize models with prompt engineering
  • 2. Discover and deploy foundation models
  • 3. Fine-tune models
  • 4. Evaluate model performance and safety

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Microsoft Azure AI Fundamentals AI-901 Prüfungsfragen mit Lösungen (Q66-Q71):

66. Frage
You are developing an AI-powered customer support application.
Which task is an example of the Microsoft responsible AI principle of inclusiveness?

Antwort: C

Begründung:
The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.
Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness.
Why the other options are incorrect:
A . Provide explanations about how predictions are generated = Transparency C . Evaluate model outputs across demographic groups to reduce bias = Fairness D . Encrypt stored customer data and restrict access by using role-based controls = Privacy and security


67. Frage
Your company has thousands of recorded customer support calls in multiple languages stored as audio files in Azure Storage.
You need to generate text transcripts of all the recordings.
Which Azure Speech in Foundry Tools capability should you use?

Antwort: D

Begründung:
For thousands of recorded support calls stored as audio files in Azure Storage, the correct capability is speech to text batch transcription.
Microsoft states that batch transcription is designed to transcribe a large amount of audio data in storage, including audio files in Azure Blob Storage, and that files can be processed concurrently to reduce turnaround time.
Real-time transcription is for live audio, not large stored batches. Text to speech converts text into audio. Speech translation translates speech between languages, but the requirement is to generate transcripts.


68. Frage
You deploy the Azure OpenAI service to generate images.
You need to ensure that the service provides the highest level of protection against harmful content.
What should you do?

Antwort: A

Begründung:
Azure OpenAI, Content filtering
Azure OpenAI includes a content filtering system that works alongside core models, including image generation models. This system works by running both the prompt and completion through a set of classification models designed to detect and prevent the output of harmful content.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter


69. Frage
Select the answer that correct completes the sentence.

Antwort:

Begründung:

Explanation:
Azure Text Analytics client library
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis features include sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics capabilities. These are accessed by using the Azure Text Analytics client library.
The highlighted Azure Speech SDK option is incorrect because it is used for speech workloads, such as speech recognition and speech synthesis, not text analysis.


70. Frage
You need to identify harmful content in a generative AI solution that uses Azure OpenAI Service.
What should you use?

Antwort: A

Begründung:
Azure AI Content Safety is a new Azure AI Service that can detect harmful user-generated and AI-generated content. This service is being integrated across MS products, including Azure OpenAI Service and Machine Learning prompt flow.
Reference:
https://ivanatilca.medium.com/testing-azure-ai-content-safety-ccd512f9b350


71. Frage
......

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