AI-901 Exam with Accurate Microsoft Azure AI Fundamentals PDF Questions

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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q27-Q32):

NEW QUESTION # 27
What should you use to identify similar faces in a set of images?

Answer: A

Explanation:
Correct:
* Azure AI Vision
Incorrect:
* Azure AI Custom Vision
* Azure AI Language
* Azure OpenAI Service
* Azure OpenAI in Foundry Models
Note: Azure AI Foundry, Vision, Find similar faces
To identify similar faces across different images using Azure, the Azure AI Face service is the most suitable choice. This service leverages AI algorithms to detect, recognize, and analyze human faces, including features like face grouping based on visual similarity.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/find-similar-faces


NEW QUESTION # 28
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 29
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Statement
Answer
Defining an AI system for a typical user profile is sufficient to support inclusiveness.
No
A high number of active users means an AI system provides an inclusive experience.
No
Providing accessible interfaces and language options helps support inclusiveness in an AI system.
Yes
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is No, No, Yes .
Microsoft defines inclusiveness as designing AI systems so they work effectively for the full range of people they are intended to serve. The current AI-901 study guide explicitly includes the ability to describe considerations for inclusiveness in an AI solution as part of the Responsible AI objective domain.
The first statement is No because designing only for a hypothetical or "typical" user can exclude people with different abilities, languages, interaction styles, cultural backgrounds, or accessibility requirements. Inclusive design deliberately considers diverse and potentially underserved users.
The second statement is No because the number of active users is an adoption or usage metric. A system may have millions of users while still creating accessibility barriers or excluding particular groups. Usage volume therefore does not demonstrate inclusiveness.
The third statement is Yes . Accessible interfaces, assistive-technology compatibility, and suitable language options reduce barriers and broaden the range of people who can effectively interact with an AI system.
Microsoft specifically emphasizes accessibility and supporting varied user needs as core elements of inclusive AI design.


NEW QUESTION # 30
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: An Azure Content Understanding in Foundry Tools analyzer returns results in the HTML format.
= No
Azure Content Understanding returns analysis results as structured output, typically JSON, not HTML.
Microsoft documentation states that the Content Understanding API returns analysis results in a structured JSON format.
Statement 2: The Content Understanding REST API analyzes content synchronously and returns results in the same response. = No Content Understanding analysis is asynchronous. The quickstart states that the response header includes an Operation-Location field, which is used to retrieve the results of the asynchronous analysis operation.
Statement 3: Azure Content Understanding in Foundry Tools can transform documents, images, audio, and video files into structured output. = Yes Microsoft describes Azure AI Content Understanding as processing documents, images, videos, and audio, and transforming them into user-defined output formats.


NEW QUESTION # 31
Verifying that machine learning models do NOT show racial or gender bias is an example of which Microsoft responsible AI principle?

Answer: A

Explanation:
Fairness and inclusiveness in Azure Machine Learning: The fairness assessment component of the Responsible AI dashboard enables data scientists and developers to assess model fairness across sensitive groups defined in terms of gender, ethnicity, age, and other characteristics.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2


NEW QUESTION # 32
......

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