AI-901최신덤프샘플문제다운 & AI-901인증시험인기덤프

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Microsoft AI-901 Exam Syllabus Topics:

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

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AI-901인증시험 인기덤프 - AI-901최신버전 인기덤프

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최신 Microsoft Certified: Azure AI Fundamentals AI-901 무료샘플문제 (Q56-Q61):

질문 # 56
You are building an AI-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible AI principle?

A. fairness
B. inclusiveness
C. transparency
D. accountability

정답：C

설명：
Transparency: AI systems should be transparent and understandable. AI systems might help inform decisions that have great impact on people's lives, so it's important that people understand how these decisions are made.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai

질문 # 57
Hotspot Question
Select the answer that correctly completes the sentence.

정답：

설명：

Explanation:
Box: a multimodal
Processing prompts that contain images requires you to deploy and use a multimodal model (such as GPT-4o) via Microsoft Foundry.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/flow-process-image

질문 # 58
You have the process shown in the following exhibit.

Which type of AI solution is shown in the diagram?

A. an agent
B. a computer vision application
C. a machine learning model
D. a sentiment analysis solution

정답：A

설명：
An agent answers a question.

질문 # 59
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. Agentic Ai
B. information extraction
C. computer vision
D. generative Al

정답：A

설명：
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.

질문 # 60
Select the answer that correctly completes the sentence.

정답：

설명：

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.

질문 # 61
......

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