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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)	15-20%	- Identify features of common NLP workload scenarios 1. Identify features and uses for sentiment analysis 2. Identify features and uses for key phrase extraction 3. Identify features and uses for entity recognition 4. Identify features and uses for translation 5. Identify features and uses for speech recognition and synthesis 6. Identify features and uses for language modeling - 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
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 AI Foundry 2. Describe features and capabilities of Azure OpenAI service 3. Describe features and capabilities of Azure AI Foundry model catalog - 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
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 semantic segmentation solutions 3. Identify features of image analysis solutions 4. Identify features of optical character recognition (OCR) solutions 5. Identify features of object detection solutions 6. Identify features of image classification solutions - 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
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 Natural Language Processing (NLP) workloads 3. Identify features of content moderation and moderation workloads 4. Identify features of generative AI workloads 5. Identify features of knowledge mining workloads 6. Identify features of computer vision workloads - Identify guiding principles for responsible AI 1. Describe considerations for privacy and security in an AI solution 2. Describe considerations for reliability and safety in an AI solution 3. Describe considerations for accountability in an AI solution 4. Describe considerations for fairness in an AI solution 5. Describe considerations for inclusiveness in an AI solution 6. Describe considerations for transparency in an AI solution
Describe fundamental principles of machine learning on Azure (15-20%)	15-20%	- 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 data and compute services for data science and machine learning 2. Describe model management and deployment capabilities in Azure Machine Learning 3. Describe capabilities of automated machine learning - Identify common machine learning techniques 1. Identify features of deep learning techniques 2. Identify regression machine learning scenarios 3. Identify classification machine learning scenarios 4. Identify clustering machine learning scenarios

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Microsoft Azure AI Fundamentals Sample Questions (Q91-Q96):

NEW QUESTION # 91
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?

A. Model leaderboard
B. Evaluator catalog
C. Tools
D. Compliance

Answer: A

Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.

NEW QUESTION # 92
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: Voice Live returns only transcribed text. = No
Voice Live is not limited to transcription. Microsoft documentation states that the Voice Live API supports real-time bidirectional voice applications, including speech recognition, text-to-speech synthesis, avatar streaming, animation data, and audio processing.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services.
= No
Voice Live provides a single real-time voice API experience rather than requiring separate STT and TTS implementations for the conversational loop. Microsoft describes live AI voice conversations as combining speech capabilities for real-time interaction, and the Voice Live API includes speech recognition and text-to- speech synthesis features.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes This is correct. Microsoft's guidance explains that Azure OpenAI audio/realtime capabilities are for scenarios that combine audio with language understanding, reasoning, or generation in a single model call, and Voice Live supports real-time voice-enabled applications over WebSocket connections.

NEW QUESTION # 93
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?

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

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.

NEW QUESTION # 94
Which Azure OpenAI model can be used to develop code?

A. DALL-E
B. GPT-4-32k
C. microsoft-swinv2-base-patch4-window12-192-22k
D. Whisper

Answer: C

Explanation:
The GPT-4 32k model can be used to develop programming code. It has a large context window, allowing it to process more information and generate more complex code. The model has shown strong performance in coding tasks, even surpassing its predecessor, o3-mini, in some domains.
Reference:
https://vivekupadhyay1.medium.com/openai-chat-gpt-4-32k-detailed-document-ce3486e4786e

NEW QUESTION # 95
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 96
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