Microsoft AI-901 Latest Exam Price - Dumps AI-901 Vce

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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Describe Artificial Intelligence workloads and considerations (15-20%)15-20%- Identify features of common AI workloads
  • 1. Identify features of computer vision workloads
  • 2. Identify features of Natural Language Processing (NLP) workloads
  • 3. Identify features of generative AI workloads
  • 4. Identify features of content moderation and moderation workloads
  • 5. Identify features of knowledge mining workloads
  • 6. Identify features of document intelligence workloads
- Identify guiding principles for responsible AI
  • 1. Describe considerations for inclusiveness in an AI solution
  • 2. Describe considerations for privacy and security in an AI solution
  • 3. Describe considerations for fairness in an AI solution
  • 4. Describe considerations for transparency in an AI solution
  • 5. Describe considerations for reliability and safety in an AI solution
  • 6. Describe considerations for accountability in an AI solution
Topic 2: Describe features of computer vision workloads on Azure (15-20%)15-20%- Identify common types of computer vision solution
  • 1. Identify features of semantic segmentation solutions
  • 2. Identify features of image analysis solutions
  • 3. Identify features of face detection and identification solutions
  • 4. Identify features of image classification solutions
  • 5. Identify features of optical character recognition (OCR) solutions
  • 6. Identify features of object detection solutions
- Identify Azure tools and services for computer vision tasks
  • 1. Describe capabilities of the Azure AI Vision service
  • 2. Describe capabilities of the Azure AI Face detection service
Topic 3: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)15-20%- Identify Azure tools and services for NLP workloads
  • 1. Describe capabilities of the Azure AI Language service
  • 2. Describe capabilities of the Azure AI Speech service
- Identify features of common NLP workload scenarios
  • 1. Identify features and uses for language modeling
  • 2. Identify features and uses for entity recognition
  • 3. Identify features and uses for translation
  • 4. Identify features and uses for key phrase extraction
  • 5. Identify features and uses for sentiment analysis
  • 6. Identify features and uses for speech recognition and synthesis
Topic 4: Describe features of generative AI workloads on Azure (20-25%)20-25%- Identify generative AI services and capabilities in Microsoft Azure
  • 1. Describe features and capabilities of Azure AI Foundry model catalog
  • 2. Describe features and capabilities of Azure OpenAI service
  • 3. Describe features and capabilities of Azure AI Foundry
- Identify features of generative AI solutions
  • 1. Identify features of generative AI models
  • 2. Identify common scenarios for generative AI
  • 3. Identify responsible AI considerations for generative AI
Topic 5: Describe fundamental principles of machine learning on Azure (15-20%)15-20%- Describe Azure Machine Learning capabilities
  • 1. Describe model management and deployment capabilities in Azure Machine Learning
  • 2. Describe capabilities of automated machine learning
  • 3. Describe data and compute services for data science and machine learning
- Identify common machine learning techniques
  • 1. Identify classification machine learning scenarios
  • 2. Identify features of deep learning techniques
  • 3. Identify clustering machine learning scenarios
  • 4. Identify regression machine learning scenarios
- Describe core machine learning concepts
  • 1. Identify features and labels for training data in machine learning
  • 2. Describe how training and validation datasets are used in machine learning

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Microsoft - The Best AI-901 - Microsoft Azure AI Fundamentals Latest Exam Price

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Microsoft Azure AI Fundamentals Sample Questions (Q130-Q135):

NEW QUESTION # 130
You are using the Azure Speech SDK to develop a Python application that supports real-time spoken conversations. Which Azure speech class should you use to configure the connection to the Azure Speech service?

Answer: C


NEW QUESTION # 131
Hotspot Question
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 132
You are reviewing best practices for using AI at your company.
Which Microsoft responsible AI principle is each task an example of? To answer, drag the appropriate principles to the correct tasks. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct match is worth one point.

Answer:

Explanation:

Explanation:
* Fairness
* Privacy and security
* Transparency
* Reliability and safety
Comprehensive and Detailed Explanation with all Azure AI documents : =
Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups:
Fairness
Microsoft's responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.
Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.
Informing users when they are interacting with an AI system and explaining the system's capabilities and limitations: Transparency The transparency principle requires that people understand when they are interacting with AI and understand the system's purpose, capabilities, and limitations.
Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.


NEW QUESTION # 133
You plan to create an AI application by using Azure AI Foundry. The solution will be deployed to dedicated virtual machines. Which deployment option should you use?

Answer: D

Explanation:
Deployment options
Azure AI Foundry provides several deployment options depending on the type of models and resources you need to provision. The following deployment options are available:
* Standard deployment in Azure AI Foundry resources
* Deployment to serverless API endpoints
* Deployment to managed computes
Serverless API endpoint
This deployment option is available only in Azure AI hub resources. It allows you to create dedicated endpoints to host the model, accessible through an API. Azure AI Foundry Models support serverless API endpoints with pay-as-you-go billing, and you can create only regional deployments for serverless API endpoints.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/deployments-overview


NEW QUESTION # 134
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: Voice Live returns only transcribed text. = No
Voice Live is not limited to transcription. Microsoft documentation states that the Voice Live API supports real-time bidirectional voice applications, including speech recognition, text-to-speech synthesis, avatar streaming, animation data, and audio processing.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services. = No Voice Live provides a single real-time voice API experience rather than requiring separate STT and TTS implementations for the conversational loop. Microsoft describes live AI voice conversations as combining speech capabilities for real-time interaction, and the Voice Live API includes speech recognition and text-to- speech synthesis features.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes This is correct. Microsoft's guidance explains that Azure OpenAI audio/realtime capabilities are for scenarios that combine audio with language understanding, reasoning, or generation in a single model call, and Voice Live supports real-time voice-enabled applications over WebSocket connections.


NEW QUESTION # 135
......

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