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

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Microsoft Azure AI Fundamentals Sample Questions (Q56-Q61):

NEW QUESTION # 56
Match the principles of responsible Al to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle 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 selection is worth one point.

Answer:

Explanation:

Explanation:
The system must not discriminate based on gender, race, or age. = Fairness This requirement maps to fairness because fairness means AI systems should avoid unfair bias and treat people equitably across demographic groups.
Personal data must be visible only to approved users. = Privacy and security This requirement maps to privacy and security because it focuses on protecting personal data and restricting access to authorized users only.
Automated decision-making processes must be recorded so that approved users can identify why a decision was made. = Transparency This requirement maps to transparency because recording decision-making processes helps users understand and explain how or why an AI-supported decision was made.

NEW QUESTION # 57
Hotspot Question
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 58
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 59
You plan to deploy a generative AI model to a Microsoft Foundry project in the East US Azure region. What should you check first?

A. the token limit
B. the authentication method
C. Model availability
D. REST API support

Answer: C

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 # 60
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:

NEW QUESTION # 61
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