Vce Microsoft AI-901 Format, 100% AI-901 Correct Answers

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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
Topic 2: 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 Speech service
  • 2. Describe capabilities of the Azure AI Language service
- Identify features of common NLP workload scenarios
  • 1. Identify features and uses for entity recognition
  • 2. Identify features and uses for translation
  • 3. Identify features and uses for sentiment analysis
  • 4. Identify features and uses for key phrase extraction
  • 5. Identify features and uses for language modeling
  • 6. Identify features and uses for speech recognition and synthesis
Topic 3: Describe features of computer vision workloads on Azure (15-20%)15-20%- Identify common types of computer vision solution
  • 1. Identify features of image classification solutions
  • 2. Identify features of image analysis solutions
  • 3. Identify features of face detection and identification solutions
  • 4. Identify features of object detection solutions
  • 5. Identify features of semantic segmentation solutions
  • 6. Identify features of optical character recognition (OCR) 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 4: Describe features of generative AI workloads on Azure (20-25%)20-25%- Identify features of generative AI solutions
  • 1. Identify common scenarios for generative AI
  • 2. Identify responsible AI considerations for generative AI
  • 3. Identify features of generative AI models
- Identify generative AI services and capabilities in Microsoft Azure
  • 1. Describe features and capabilities of Azure OpenAI service
  • 2. Describe features and capabilities of Azure AI Foundry model catalog
  • 3. Describe features and capabilities of Azure AI Foundry
Topic 5: Describe Artificial Intelligence workloads and considerations (15-20%)15-20%- Identify features of common AI workloads
  • 1. Identify features of document intelligence workloads
  • 2. Identify features of generative AI workloads
  • 3. Identify features of Natural Language Processing (NLP) workloads
  • 4. Identify features of knowledge mining workloads
  • 5. Identify features of content moderation and moderation workloads
  • 6. Identify features of computer vision workloads
- Identify guiding principles for responsible AI
  • 1. Describe considerations for reliability and safety in an AI solution
  • 2. Describe considerations for accountability in an AI solution
  • 3. Describe considerations for privacy and security in an AI solution
  • 4. Describe considerations for transparency in an AI solution
  • 5. Describe considerations for fairness in an AI solution
  • 6. Describe considerations for inclusiveness in an AI solution

>> Vce Microsoft AI-901 Format <<

100% AI-901 Correct Answers, AI-901 Certification Test Questions

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Microsoft Azure AI Fundamentals Sample Questions (Q55-Q60):

NEW QUESTION # 55
You deploy an Al system to assist with hiring decisions.
Company policy requires that human reviewers oversee Al-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible Al principle is this an example of?

Answer: B

Explanation:
Requiring human reviewers to oversee AI-generated hiring recommendations and remain responsible for the final hiring decisions is an example of the Microsoft responsible AI principle of accountability .
Accountability means people and organizations remain responsible for AI systems, their decisions, and their effects. In this scenario, the AI system assists with recommendations, but humans retain oversight and responsibility for the final decision.


NEW QUESTION # 56
You are developing an application named App1 that analyzes customer feedback.
App1 will call Azure Language in Foundry Tools from a generative AI agent in Microsoft Foundry.
You need to ensure that the agent can access Azure Language text analysis.
What should you configure?

Answer: A

Explanation:
The Azure Language MCP (Model Context Protocol) server needs to be configured so your generative AI agent can utilize text analysis tools (like sentiment analysis, keyphrase extraction, and language detection).Azure Language in Foundry Tools exposes its capabilities as standardized tools using the MCP framework. This means your agent cannot call the traditional REST API directly; it must route requests through an MCP server configuration.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview


NEW QUESTION # 57
You plan to deploy a generative AI model to a Microsoft Foundry project in the East US Azure region. What should you check first?

Answer: A

Explanation:
Model availability should be checked first. You must confirm that the specific generative AI model is supported and offered in the East US region before checking your token rate limits or allocation quota, as a model cannot be deployed in a region where it does not exist.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/quotas-limits


NEW QUESTION # 58
Drag and Drop Question
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:


NEW QUESTION # 59
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: Fairness can be achieved by focusing solely on improving the overall accuracy of an AI model. = No Overall accuracy alone does not guarantee fairness. A model can have high overall accuracy while still producing biased or lower-quality outcomes for specific demographic groups. Fairness requires evaluating and reducing differences in model behavior across groups.
Statement 2: Evaluating AI system outputs to identify and reduce bias across demographic groups supports fairness. = Yes This statement is correct. The Microsoft responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias. Evaluating outputs across demographic groups helps identify and reduce bias.
Statement 3: Ensuring fairness for an AI system means that all users always receive the same output from the system. = No Fairness does not mean identical outputs for all users. It means the system should avoid unfair bias and provide equitable treatment. Different users may receive different outputs when differences are relevant and appropriate.


NEW QUESTION # 60
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