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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Describe Artificial Intelligence workloads and considerations	20-25%	- Responsible AI principles 1. Transparency and accountability 2. Fairness 3. Privacy and security 4. Reliability and safety 5. Inclusiveness - Identify features of common AI workloads 1. Computer vision use cases 2. Conversational AI and chatbots 3. Document processing and extraction 4. Natural language processing use cases
Topic 2: Describe fundamental principles of machine learning on Azure	25-30%	- Azure Machine Learning basics 1. AutoML concepts 2. Azure ML workspace components - Machine learning concepts 1. Regression and classification 2. Training and evaluation concepts 3. Supervised vs unsupervised learning
Topic 3: Describe features of generative AI workloads on Azure	10-15%	- Generative AI concepts 1. Large language models (LLMs) 2. Responsible use of generative AI 3. Prompt engineering basics - Azure OpenAI and generative services 1. Azure OpenAI Service capabilities 2. Use cases: chatbots, content generation, summarization
Topic 4: Describe features of natural language processing (NLP) workloads on Azure	15-20%	- Text analysis 1. Language detection 2. Sentiment analysis 3. Key phrase extraction - Azure NLP services 1. Speech services (speech-to-text, text-to-speech) 2. Azure AI Language
Topic 5: Describe features of computer vision workloads on Azure	15-20%	- Image analysis 1. Optical character recognition (OCR) 2. Object detection 3. Image classification - Azure AI Vision services 1. Face detection and analysis (limited use cases) 2. Azure AI Vision

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Microsoft Azure AI Fundamentals Sample Questions (Q25-Q30):

NEW QUESTION # 25
Hotspot Question
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:
Box 1: Yes
Yes, image analysis capabilities in Azure Vision in Foundry Tools can perform optical character recognition (OCR). The built-in Read OCR engine extracts both printed and handwritten text in multiple languages and writing styles from images and documents.
Box 2: Yes
Yes, image analysis capabilities in Azure Vision in Foundry Tools can automatically generate concise one-sentence human-readable captions and descriptive content tags for thousands of recognizable objects, living beings, actions, and settings.
Box 3: No
Image analysis capabilities in Foundry Tools are designed to process, interpret, and extract insights from existing images, rather than create new ones.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-image-analysis

NEW QUESTION # 26
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 # 27
You need to build an AI solution that responds to user queries with detailed written explanations.
Which type of AI model should you use?

A. a text generation model
B. an image generation model
C. an embedding model
D. a speech recognition model

Answer: A

NEW QUESTION # 28
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini-gpt.
You need to connect to my-mini-gpt from an application.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 29
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?

A. translation
B. speech recognition
C. language modeling
D. key phrase extraction

Answer: A

NEW QUESTION # 30
......

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