한번에Microsoft인증AI-103시험을 패스하고 싶으시다면 완전 페펙트한 준비가 필요합니다. 완벽한 관연 지식터득은 물론입니다. 우리ITDumpsKR의 자료들은 여러분의 이런 시험준비에 많은 도움이 될 것입니다.
| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
저희 ITDumpsKR는 국제공인 IT자격증 취득을 목표를 하고 있는 여러분들을 위해 적중율 좋은 시험대비 덤프를 제공해드립니다. Microsoft AI-103 시험을 패스하여 자격증을 취득하려는 분은 저희 사이트에서 출시한Microsoft AI-103덤프의 문제와 답만 잘 기억하시면 한방에 시험패스 할수 있습니다. 해당 과목 사이트에서 데모문제를 다운바다 보시면 덤프품질을 검증할수 있습니다.결제하시면 바로 다운가능하기에 덤프파일을 가장 빠른 시간에 받아볼수 있습니다.
질문 # 111
You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
temperature = 0
output_config = { " effort " : " high " }
The correct configuration is temperature = 0 and output_config = { " effort " : " high " }. The requirement is to reduce small wording variations that are breaking automated validation. In chat completion requests, temperature controls sampling randomness. Microsoft's Azure OpenAI reference states that temperature ranges between 0 and 2, and that higher values make output more random while lower values make output more focused and deterministic. Therefore, the most stable setting from the available choices is 0, because it minimizes randomness and improves repeatability for validation-sensitive response patterns.
The solution must also maximize reasoning quality. The code already enables thinking with thinking={ " type
" : " enabled " }, so the remaining reasoning-quality control is the effort setting. Microsoft Foundry model guidance states that the effort parameter controls the quality/cost tradeoff and supports low, medium, and high effort levels. Selecting " high " maximizes reasoning quality among the available options.
Using temperature values of 1 or 2 would increase variability and make validation mismatches more likely.
Selecting low or medium effort would not meet the requirement to maximize reasoning quality. Reference topics: Microsoft Foundry model inference, chat model parameters, temperature, thinking, effort, and output stability.
질문 # 112
In mid-2026 you are starting a brand-new agentic application on Azure and want to build on the generally available, supported entry point for Foundry agents. Which API should you target?
정답：B
설명：
The Responses API is the generally available single entry point for Foundry Agent Service and supports both prompt agents and hosted agents. It is the API Microsoft directs new agentic development towards.
질문 # 113
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent uses a stored access key to retrieve secrets from an Azure key vault, which violates a keyless-credentials requirement.
You need to ensure that the agent can retrieve the secrets. The solution must follow the principle of least privilege.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Box 1: Enable a system-assigned managed identity at the project level
The correct configuration is to enable a system-assigned managed identity at the project level.
This is the correct approach. Azure AI Foundry (formerly Azure Machine Learning) allows you to enable a system-assigned managed identity directly at the project level. This identity acts as a keyless credential native to Azure. To follow the principle of least privilege, you then assign this project-level identity a specific role (such as Key Vault Secrets User) restricted only to the required Azure Key Vault. This ensures that the agent can retrieve secrets securely without exposing credentials to other projects or resources Box 2: Assign the Key Vault Secrets User role to the managed identity To comply with the keyless-credentials requirement and follow the principle of least privilege, you should configure the Key Vault authorization method to assign the Key Vault Secrets User role to the managed identity using Azure Role-Based Access Control (Azure RBAC).
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/ai-gateway
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
질문 # 114
You have a Microsoft Foundry project that generates product marketing images from text prompts.
After publishing several images, the legal team at your company identifies a competitor's logo on a sign in the background of an image.
You need to remove only the logo, while preserving the rest of the image.
What should you do?
정답：A
설명：
To remove a logo from a generated image while preserving the rest of the content, you should use AI-powered inpainting tools or standard object removal features in professional image editing software.
Microsoft Designer or Azure AI (In-Engine Fixes)
If your Foundry project is built on top of Azure OpenAI Service or Dall-E, you can often handle this programmatically or through related first-party tools.
Generative Erase / Inpainting: Use Microsoft Designer's "Erase" tool. Brush over the logo, and the AI will replace it by seamlessly blending the background.
API Inpainting: If you have access to the underlying image generation API (like DALL-E 3 or Stable Diffusion), use the Inpainting API. Pass the original image along with a black-and-white mask highlighting the logo, and prompt it to "fill the background naturally." Reference:
https://starryai.com/en/blog/mai-image-1
질문 # 115
You need to measure the public perception of your brand on social media by using natural language processing. Which Azure service should you use?
정답：C
설명：
Azure Language in Foundry Tools provides sentiment analysis and opinion mining , which are the appropriate natural language processing capabilities for evaluating public perception. Sentiment analysis processes unstructured social-media text and assigns classifications such as positive, neutral, or negative, together with confidence scores. Opinion mining extends this analysis by associating expressed sentiment with particular aspects, attributes, products, or services mentioned in the text. This allows the application to determine not only the overall attitude toward the brand but also which specific brand characteristics are receiving favorable or unfavorable reactions.
Azure Document Intelligence is intended primarily for extracting text, fields, tables, and structures from documents. Content Safety detects potentially harmful material rather than measuring consumer opinion.
Azure Vision processes visual content and is not the primary service for sentiment classification of written social-media posts.
Azure Language can be accessed through REST APIs, client libraries, or supported containers. The application submits the social-media text and receives document-level and sentence-level sentiment results that can be aggregated into brand-perception metrics, dashboards, or alerts.
Study Guide alignment: Implement text analysis solutions - perform sentiment analysis and opinion mining, process unstructured text, and interpret confidence scores and sentiment classifications.
질문 # 116
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