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The Microsoft modern job market is becoming more and more competitive and challenging and if you are not ready for it then you cannot pursue a rewarding career. Take a smart move right now and enroll in the Microsoft Azure AI Fundamentals (AI-901) certification exam and strive hard to pass the Microsoft Azure AI Fundamentals (AI-901) certification exam. The Microsoft Azure AI Fundamentals (AI-901) certification exam offers you a unique opportunity to learn new in-demand skills and knowledge.

Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q16-Q21):

NEW QUESTION # 16
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?

Answer: C

Explanation:
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.
Why the other options are incorrect:
B . image analysis is used to examine and interpret existing images.
C . object detection is used to identify and locate objects within an existing image.
D . optical character recognition (OCR) is used to extract text from images or scanned documents.
Since the solution must generate new visual content from user-provided descriptions, the correct answer is:
A . image generation


NEW QUESTION # 17
Your company provides users with clear information about an AI system's purpose, functionality, and limitations. Which Microsoft responsible AI principle is this an example of?

Answer: C

Explanation:
Transparency is the Microsoft responsible AI principle that provides clear information about an AI system's purpose, how it works, and its limitations.
System Purpose: Explains what the AI is designed to do and how it helps users.
Functionality: Shows how the model makes decisions or generates content.
Limitations: Warns users about potential errors, blind spots, or times when the AI might be wrong.
User Awareness: Ensures people know they are interacting with an AI system.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach


NEW QUESTION # 18
You have a website that includes customer reviews.
You need to store the reviews in English and present the reviews to users in their respective language based on each user ' s geographical location.
Which type of natural language processing workload should you use?

Answer: B


NEW QUESTION # 19
What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: A,C,E

Explanation:
Microsoft's Six Responsible AI Principles
[C] Fairness: AI systems should treat all people equitably and avoid bias or discrimination.
[D] Reliability and Safety: Systems must operate dependably, consistently, and safely without causing harm.
Privacy and Security: AI must protect user data securely and respect personal privacy.
[E] Inclusiveness: Design should empower and engage diverse communities and users of all abilities.
Transparency: AI behavior and decision-making processes should be understandable to users.
Accountability: Creators and deployers of AI are responsible for how their systems operate.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach


NEW QUESTION # 20
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?

Answer: D

Explanation:
An analyzer in Azure Content Understanding within Foundry Tools can be used for this scenario.
Prebuilt Invoice Support: It offers prebuilt invoice analyzer templates specifically trained to identify common transactional data out of the box.
Schema-Driven Extraction: Instead of manual prompt engineering or tedious labeling, you can supply a strict JSON schema containing the specific fields you need.
Nested Values & Layouts: It handles hierarchical, nested elements (such as itemized tables, nested line items, or customer details) and maps them cleanly into arrays or structured objects.
Web App Integration: Activating the analyzer creates a highly scalable REST API endpoint. Your invoice-processing web app can query this endpoint to receive clean, machine-readable structured JSON payloads ready for downstream expense calculation.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/overview


NEW QUESTION # 21
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