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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 responsible AI considerations for generative AI 2. Identify features of generative AI models 3. Identify common scenarios for generative AI - 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
Describe features of computer vision workloads on Azure (15-20%)	15-20%	- Identify common types of computer vision solution 1. Identify features of semantic segmentation solutions 2. Identify features of image classification solutions 3. Identify features of object detection solutions 4. Identify features of image analysis solutions 5. Identify features of optical character recognition (OCR) solutions 6. Identify features of face detection and identification 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 fundamental principles of machine learning on Azure (15-20%)	15-20%	- Identify common machine learning techniques 1. Identify features of deep learning techniques 2. Identify clustering machine learning scenarios 3. Identify classification machine learning scenarios 4. Identify regression machine learning scenarios - 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 - Describe Azure Machine Learning capabilities 1. Describe data and compute services for data science and machine learning 2. Describe capabilities of automated 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 features of common NLP workload scenarios 1. Identify features and uses for sentiment analysis 2. Identify features and uses for speech recognition and synthesis 3. Identify features and uses for key phrase extraction 4. Identify features and uses for translation 5. Identify features and uses for language modeling 6. Identify features and uses for entity recognition - 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 Artificial Intelligence workloads and considerations (15-20%)	15-20%	- Identify features of common AI workloads 1. Identify features of computer vision workloads 2. Identify features of Natural Language Processing (NLP) workloads 3. Identify features of content moderation and moderation workloads 4. Identify features of knowledge mining workloads 5. Identify features of generative AI workloads 6. Identify features of document intelligence workloads - 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 accountability in an AI solution 5. Describe considerations for privacy and security in an AI solution 6. Describe considerations for fairness in an AI solution

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Microsoft Azure AI Fundamentals Sample Questions (Q121-Q126):

NEW QUESTION # 121
You plan to create an AI application that will use Azure OpenAI Service. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?

A. data zone standard
B. global batch
C. provisioned
D. standard

Answer: C

Explanation:
When a specific amount of throughput is needed with Azure OpenAI Service, you should use Provisioned Throughput Units (PTUs) for your deployment. PTUs allow you to reserve a fixed amount of processing capacity for your models, ensuring consistent performance and avoiding potential throttling or rate limits.
In contrast, the "Standard" deployment option in Azure OpenAI Service uses a "pay-as-you-go" model, where you are billed for the amount of usage. While this can be suitable for applications with fluctuating usage patterns, it does not provide the same level of throughput control as PTUs.
In summary: When you need to allocate a specific amount of throughput with Azure OpenAI Service, choose the "Provisioned" deployment type, which allows you to use PTUs.
Reference:
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-
services/openai/concepts/provisioned-throughput.md

NEW QUESTION # 122
You are developing an application that will coordinate a travel-booking workflow by gathering user preferences, searching for options, requesting approval, and booking services. The solution will maintain context across the steps.
Which type of Al workload is this an example of?

A. information extraction
B. computer vision
C. generative Al
D. Agentic Ai

Answer: D

Explanation:
The described solution coordinates a multi-step workflow : gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI
.
Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.
Why the other options are incorrect:
A). information extraction extracts structured data from content.
C). computer vision analyzes images or video.
D). generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI .

NEW QUESTION # 123
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

Ongoing monitoring, feedback, and evaluation are goals of the Reliability & Safety principle.
The Microsoft responsible AI principle of Reliability and safety focuses on ensuring that AI systems operate consistently, safely, and as intended across different conditions. This includes testing, monitoring, collecting feedback, and evaluating system performance over time.
Why the other options are incorrect:
Human oversight and control aligns more closely with accountability.
Data governance and management aligns more closely with privacy and security.
The minimization of stereotyping, demeaning, and erasing outputs aligns more closely with fairness and inclusiveness.

NEW QUESTION # 124
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.
Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.
The other options are incorrect:
diarization identifies or separates speakers, such as "Speaker 1" and "Speaker 2." key phrase extraction identifies important phrases from text after text exists.
synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.

NEW QUESTION # 125
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model.
To call a deployed model from an application, the application must send requests to the deployed model's endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.
The other options are incorrect:
evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.
Foundry playground is an interactive testing environment, not the production target for application requests.
training dataset of the model is used for training or fine-tuning, not for sending inference requests.
Therefore, the correct answer is endpoint of the model.

NEW QUESTION # 126
......

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