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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Describe features of generative AI workloads on Azure (20-25%)	20-25%	- Identify features of generative AI solutions 1. Identify common scenarios for generative AI 2. Identify responsible AI considerations for generative AI 3. Identify features of generative AI models - Identify generative AI services and capabilities in Microsoft Azure 1. Describe features and capabilities of Azure OpenAI service 2. Describe features and capabilities of Azure AI Foundry 3. Describe features and capabilities of Azure AI Foundry model catalog
Describe fundamental principles of machine learning on Azure (15-20%)	15-20%	- 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 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 - 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 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 speech recognition and synthesis 2. Identify features and uses for key phrase extraction 3. Identify features and uses for translation 4. Identify features and uses for entity recognition 5. Identify features and uses for sentiment analysis 6. Identify features and uses for language modeling
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 object detection solutions 2. Identify features of semantic segmentation solutions 3. Identify features of image classification solutions 4. Identify features of optical character recognition (OCR) solutions 5. Identify features of face detection and identification solutions 6. Identify features of image analysis solutions
Describe Artificial Intelligence workloads and considerations (15-20%)	15-20%	- Identify guiding principles for responsible AI 1. Describe considerations for reliability and safety in an AI solution 2. Describe considerations for transparency in an AI solution 3. Describe considerations for inclusiveness in an AI solution 4. Describe considerations for fairness in an AI solution 5. Describe considerations for accountability in an AI solution 6. Describe considerations for privacy and security in an AI solution - Identify features of common AI workloads 1. Identify features of generative AI workloads 2. Identify features of computer vision workloads 3. Identify features of content moderation and moderation workloads 4. Identify features of document intelligence workloads 5. Identify features of Natural Language Processing (NLP) workloads 6. Identify features of knowledge mining workloads

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Free PDF Quiz High Pass-Rate AI-901 - Exam Microsoft Azure AI Fundamentals Answers

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Microsoft Azure AI Fundamentals Sample Questions (Q13-Q18):

NEW QUESTION # 13
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:

NEW QUESTION # 14
Capturing text from images is an example of which type of Al capability?

A. object detection
B. image description
C. text analysis
D. optical character recognition (OCR)

Answer: D

NEW QUESTION # 15
Which natural language processing feature can be used to identify the main talking points in customer feedback surveys?

A. key phrase extraction
B. entity recognition
C. translation
D. language detection

Answer: A

Explanation:
To identify main talking points in feedback using Azure natural language processing, the Key Phrase Extraction feature within the Azure AI Language service is the most appropriate choice.
This feature analyzes text and extracts the most relevant and important phrases, effectively highlighting the core topics or themes present in the feedback.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

NEW QUESTION # 16
You deploy an Al system to assist with hiring decisions.
Company policy requires that human reviewers oversee Al-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible Al principle is this an example of?

A. reliability and safety
B. transparency
C. accountability
D. fairness

Answer: C

Explanation:
Requiring human reviewers to oversee AI-generated hiring recommendations and remain responsible for the final hiring decisions is an example of the Microsoft responsible AI principle of accountability .
Accountability means people and organizations remain responsible for AI systems, their decisions, and their effects. In this scenario, the AI system assists with recommendations, but humans retain oversight and responsibility for the final decision.

NEW QUESTION # 17
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. a speech recognition model
C. an embedding model
D. an image generation model

Answer: A

NEW QUESTION # 18
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