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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) | 15-20% | - Identify Azure tools and services for NLP workloads
|
| Topic 2: Describe fundamental principles of machine learning on Azure (15-20%) | 15-20% | - Describe core machine learning concepts
|
| Topic 3: Describe features of computer vision workloads on Azure (15-20%) | 15-20% | - Identify common types of computer vision solution
|
| Topic 4: Describe Artificial Intelligence workloads and considerations (15-20%) | 15-20% | - Identify guiding principles for responsible AI
|
| Topic 5: Describe features of generative AI workloads on Azure (20-25%) | 20-25% | - Identify generative AI services and capabilities in Microsoft Azure
|
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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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