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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q68-Q73):

NEW QUESTION # 68
Select the answer that correctly complete the sentence.

Answer:

Explanation:

Explanation:
Azure Text Analytics client library
Comprehensive and Detailed Explanation with all Azure AI documents: =
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure AI Language text analysis features, such as sentiment analysis, named entity recognition, key phrase extraction, language detection, and text summarization, are accessed in code by using the Azure Text Analytics client library.
The other options are incorrect:
Azure AI Projects client library is used for Foundry project and agent-related workflows, not specifically Azure Language text analysis.
Azure OpenAI client library is used to call OpenAI model deployments, not Azure Language text analytics APIs.
Azure Speech SDK is used for speech-to-text and text-to-speech workloads, not text analysis.

NEW QUESTION # 69
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. language detection
D. translation

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 # 70
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 # 71
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
To define an agent ' s role and behaviors, you must configure a system prompt for the agent.
In Microsoft Foundry / Azure AI agent scenarios, the system prompt, also referred to as system instructions, defines how the agent should behave, what role it should follow, and what constraints it must observe.
The other options are incorrect:
deployment slot relates to hosting/deployment routing, not agent behavior.
embedding index is used for vector search or retrieval scenarios, not defining the agent's role.
fine tuning job changes or adapts model behavior through training, but it is not the standard configuration used to define an agent's role and behavior at runtime.
Therefore, the correct answer is system prompt.

NEW QUESTION # 72
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Box: Computer vision
Computer vision uses Optical Character Recognition (OCR) and deep learning models to detect, localize, and extract printed or handwritten text from images, turning visual pixels into editable digital data.
Reference:
https://docs.cloud.google.com/vision/docs/ocr

NEW QUESTION # 73
......

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