Microsoft AI-103 덤프는 Microsoft AI-103 시험의 모든 문제를 커버하고 있어 시험적중율이 아주 높습니다. Pass4Test는 Paypal과 몇년간의 파트너 관계를 유지하여 왔으므로 신뢰가 가는 안전한 지불방법을 제공해드립니다. Microsoft AI-103시험탈락시 제품비용 전액환불조치로 고객님의 이익을 보장해드립니다.
| Section | Objectives |
|---|---|
| Topic 1: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Topic 2: Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Topic 3: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 4: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Topic 5: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
>> Microsoft AI-103최신 덤프샘플문제 <<
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질문 # 119
You have a product support manual.
You need to build a product support chatbot based on the manual. The solution must minimize development effort and costs.
What should you use?
정답：D
설명：
Azure OpenAI On Your Data makes it easier for developers to connect, ingest and ground their enterprise data to create personalized copilots (preview) rapidly. It enhances user comprehension, expedites task completion, improves operational efficiency, and aids decision- making.
Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models. You can chat on top of and analyze your data with greater accuracy. You can specify sources to support the responses based on the latest information available in your designated data sources.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/use-your-data
질문 # 120
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
* Authenticates by using a Microsoft Entra managed identity
* Sends prompts to a deployed model by using the Azure OpenAI Responses API 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:
credential = DefaultAzureCredential
response = openai_client.responses.create
The correct authentication class is DefaultAzureCredential because the requirement is Microsoft Entra-based authentication, and this credential supports managed identity in hosted Azure environments. The Microsoft Foundry quickstart for Python shows the project client being created with AIProjectClient (endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential()), which enables the Foundry SDK to authenticate without using API keys or embedded secrets. The same guidance shows creating an OpenAI- compatible client from the project by calling project.get_openai_client().
The correct Responses API method is create because the application must send a new prompt to the deployed model and receive generated output. Microsoft's Foundry quickstart demonstrates the exact pattern: response
= openai.responses.create(model= " gpt-5-mini " , input= " ... " ), followed by reading response.output_text.
The retrieve operation is used to fetch an existing response, not submit a new inference request, and compact is not the correct method for generating a model response. AzureKeyCredential would violate the Microsoft Entra managed identity requirement, while ClientSecretCredential uses an application secret rather than managed identity. Reference topics: Microsoft Foundry SDK, AIProjectClient, Microsoft Entra authentication, DefaultAzureCredential, and Azure OpenAI Responses API.
질문 # 121
You need to ensure that the marketing department can generate videos by using the model deployed to Project2. now 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:
Call client.videos.create() to submit the prompt and start a video-generation job against the model deployment. Video generation is asynchronous: the initial response represents a job and contains properties such as its unique ID and current status rather than immediately returning the completed video. Microsoft's official Python example creates the job by using client.videos.create(model=..., prompt=...).
The application must then poll the job. After waiting between requests, client.videos.retrieve(video.id) obtains the latest state of that specific job. Polling continues until the status becomes completed, failed, or cancelled.
list would enumerate jobs rather than refresh the selected job, while download_content is used only after successful completion to retrieve the generated video file.
To comply with Contoso's prohibition on API keys, the client should use a Microsoft Entra bearer-token provider. The OpenAI Python client accepts this provider through its api_key constructor parameter even though no static API key is used.
Study Guide alignment: design and implement video-generation solutions and integrate generative workflows through Foundry SDKs.
질문 # 122
You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
- Support turn-taking dynamics, where the agent begins to generate the
speech output before the user finishes speaking.
- Operate with low latency to maintain conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?
정답：B
설명：
To achieve low latency and natural turn-taking dynamics in this specific Microsoft Foundry workflow, the best approach is to use real-time speech-to-text for incoming audio and text-to- speech for agent responses.
Low Latency: Streaming, real-time Speech-to-Text (STT) and Text-to-Speech (TTS) pipelines process audio chunks concurrently. This allows the system to analyze text and prepare responses while the user is still speaking.
Turn-Taking Support: Real-time STT systems utilize voice activity detection (VAD) and continuous streaming. This enables the agent to instantly detect pauses, interruptions, or trailing speech to naturally shift conversational turns.
Direct Reasoning Compatibility: Because your workflow requires converting input into text for reasoning (such as prompting a Large Language Model), a highly optimized text-based pipeline fits seamlessly without extra translation layers.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/priority-processing
질문 # 123
Hotspot Question
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
- Authenticates by using a Microsoft Entra managed identity
- Sends prompts to a deployed model by using the Azure OpenAI Responses API 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：
질문 # 124
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IT인증시험덤프자료를 제공해드리는 사이트는 너무나도 많습니다. 그중에서 대부분 분들이Pass4Test제품에 많은 관심과 사랑을 주고 계시는데 그 원인은 무엇일가요?바로Pass4Test에서 제공해드리는 덤프자료 품질이 제일 좋고 업데이트가 제일 빠르고 가격이 제일 저렴하고 구매후 서비스가 제일 훌륭하다는 점에 있습니다. Pass4Test 표 Microsoft인증AI-103덤프를 공부하시면 시험보는데 자신감이 생기고 시험불합격에 대한 우려도 줄어들것입니다.
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