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| Section | Objectives |
|---|---|
| Topic 1: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Topic 2: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 3: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Topic 4: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 5: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
IT인증시험은 국제적으로 인정받는 자격증을 취득하는 과정이라 난이도가 아주 높습니다. Microsoft인증 AI-103시험은 IT인증자격증을 취득하는 시험과목입니다.어떻게 하면 난이도가 높아 도전할 자신이 없는 자격증을 한방에 취득할수 있을가요? 그 답은Itexamdump에서 찾을볼수 있습니다. Itexamdump에서는 모든 IT인증시험에 대비한 고품질 시험공부가이드를 제공해드립니다. Itexamdump에서 연구제작한 Microsoft인증 AI-103덤프로Microsoft인증 AI-103시험을 준비해보세요. 시험패스가 한결 편해집니다.
질문 # 118
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：A
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 # 119
You have a Python application that uses Azure OpenAt structured outputs to extract fields from unstructured receipt text and support ticket text. The application uses the following schema.
정답：
설명：
Explanation:
* The schema requires that a receipt has a discount_code value - No
* The schema can be used as is for Azure OpenAI structured outputs - No
* The generated JSON object will preserve the property order merchant, order_number, discount_code, line_items, and total - Yes The discount_code definition permits a nullable result. Consequently, the output can contain " discount_code
" : null when the source receipt contains no discount code. The property can be structurally present without containing a substantive discount-code value; therefore, the first statement is No .
The schema cannot be submitted unchanged. Azure OpenAI structured outputs support only a defined subset of JSON Schema. Every property declared in an object must be included in that object's required array.
Optional information must instead be represented as a required property whose type includes null. Each object must also specify " additionalProperties " : false. Furthermore, although nested anyOf constructs are supported, an anyOf construct cannot be used as the root schema. A schema that violates any of these restrictions must be revised before use, making the second statement No .
The third statement is Yes . Azure OpenAI structured outputs preserve keys in the order in which the properties are defined in the supplied schema. Therefore, the receipt object will emit merchant, order_number, discount_code, line_items, and total in that sequence. This behavior supports predictable parsing and downstream integration. The relevant curriculum area is Use Azure OpenAI in Foundry Models to generate content , including application integration and structured model responses.
질문 # 120
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?
정답：D
설명：
To remedy the "invalid project ID" error when calling the Azure Speech to Text REST API within your Microsoft AI Foundry project, you must update the project property in your API request body from a plain string name/ID to the fully qualified Azure Resource URI of the project.
The Speech to Text REST API (v3.0 and later) strictly expects resource links formatted as URIs rather than individual ID strings.
*-> Step 1: Construct the Correct Project URI
You need to pass the project as an absolute object reference. Construct your project property value using the following format:text
https://<your-region>://<your-project-guid>
Use code with caution.Replace <your-region> with your actual Azure Speech resource region (e.g., eastus, westeurope).Replace <your-project-guid> with the actual system-generated Unique Identifier of your project.
Step 2: Retrieve your Project GUID
Step 3: Update your Agent / REST API Request Body
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/azure-ai-speech
질문 # 121
You have an Azure subscription.
You plan to build an app that will use the Azure OpenAI DALL-E model.
You need to deploy the model.
What should you use?
정답：A
설명：
To deploy the Azure OpenAI DALL-E model, the most appropriate tools to use are Azure AI Studio for model management and interaction, and the Azure Command-Line Interface (CLI) for deployment and configuration tasks. Azure AI Studio provides an interactive interface for working with Azure AI models, and the Azure CLI allows you to manage resources, deployments, and other Azure services from the command line.
질문 # 122
You have an Azure AI Search indexer that ingests PDF policy manuals.
Client applications must display page-level citations that have bounding polygons for both text and images.
You need to add a single built-in multimodal content extraction skill to the Azure AI Search skillset. The solution must meet the following requirements:
- Provide text and image location metadata.
- Extract tables that span multiple pages.
What should you add?
정답：A
설명：
To meet all requirements for this project, you need the Azure Content Understanding skill (integrated via Azure AI Foundry / Microsoft Foundry Tools).
1. Extracting Cross-Page Tables: A critical constraint is the ability to recognize and extract tables that span multiple pages as a single unit. The Azure Content Understanding skill natively supports this capability, whereas the older Document Layout skill outputs layout content as flattened text/markdown boundaries that break across pages, leading to information loss.
2. Page-Level Bounding Polygons: It functions as a layout-aware, built-in multimodal content extraction skill that maps out text and image location metadata (location_metadata), satisfying your client application requirement for strict page-level citation boundaries.
Reference:
https://learn.microsoft.com/en-us/azure/search/cognitive-search-skill-content-understanding
질문 # 123
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AI-103최고품질 인증시험 대비자료: https://www.itexamdump.com/AI-103.html