ExamPassdump는 여러 it인증에 관심 있고 또 응시하고 싶으신 분들에게 편리를 드립니다. 그리고 많은 분들이 이미 ExamPassdump제공하는 덤프로 it인증시험을 한번에 패스를 하였습니다. 즉 우리 ExamPassdump 덤프들은 아주 믿음이 가는 보장되는 덤프들이란 말이죠. ExamPassdump에는 베터랑의전문가들로 이루어진 연구팀이 잇습니다, 그들은 it지식과 풍부한 경험으로 여러 가지 여러분이Microsoft인증AI-103시험을 패스할 수 있을 자료 등을 만들었습니다 여러분이Microsoft인증AI-103시험에 많은 도움이AI-103될 것입니다. ExamPassdump 가 제공하는AI-103테스트버전과 문제집은 모두AI-103인증시험에 대하여 충분한 연구 끝에 만든 것이기에 무조건 한번에AI-103시험을 패스하실 수 있습니다.
| Section | Weight | Objectives |
|---|---|---|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
Microsoft인증 AI-103시험패스는 IT업계종사자들이 승진 혹은 연봉협상 혹은 이직 등 보든 면에서 날개를 가해준것과 같습니다.IT업계는 Microsoft인증 AI-103시험을 패스한 전문가를 필요로 하고 있습니다. ExamPassdump의Microsoft인증 AI-103덤프로 시험을 패스하고 자격증을 취득하여 더욱더 큰 무대로 진출해보세요.
질문 # 69
You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition.
You are approaching the expiration date of the custom speech to text model.
What is the expected behavior when the model expires?
정답：A
설명：
The correct answer is A. Speech recognition requests will fall back to the most recent base model for the same locale . Microsoft's custom speech model lifecycle guidance states that when a model expires, it is no longer available for transcription. For the custom endpoint route, speech recognition requests fall back to the most recent base model for the same locale. The documentation also warns that recognition results might still be returned, but the transcription may no longer reflect the domain-specific adaptation of the custom model.
This distinguishes real-time custom endpoint behavior from batch transcription behavior. Batch transcription requests that specify an expired model fail with a 4xx error, but that is not the route described in this question.
The agent is using a custom endpoint for real-time recognition, so fallback to the latest base model is the expected behavior. The model is not automatically deleted merely because it expires, and it does not continue to use the expired custom model indefinitely. The operational recommendation is to update the endpoint's model before expiration by redeploying the endpoint with a newer custom model. Reference topics: Azure Speech custom speech model lifecycle, custom endpoints, model expiration, real-time speech recognition, and endpoint redeployment.
질문 # 70
You are building an app that will use Azure AI to monitor workspaces for safety. You need to recommend a service that meets the following requirements:
* Generates alerts when employees enter high-risk areas
* Monitors video feeds in real time
* Minimizes development effort
What should you recommend?
정답：B
설명：
Azure Vision in Foundry Tools Spatial Analysis is designed to process real-time streaming video and analyze the presence, movement, and spatial relationships of people in physical environments. It provides predefined operations such as personcrossingpolygon, which can identify when a person enters or exits a configured zone. The resulting personZoneEnterExitEvent can be consumed by an application to trigger alerts when an employee enters an area designated as high risk.
Spatial Analysis minimizes development effort because it supplies pretrained people-detection and zone- monitoring capabilities. Developers configure camera streams, polygonal zones, and event parameters instead of collecting images, labeling objects, and training a custom model.
Image Analysis primarily evaluates individual images and does not maintain movement or zone state across a live stream. Azure AI Video Indexer extracts searchable insights from video or live-stream content, but it is not the specialized option for detecting physical zone-entry events. Custom Vision object detection would require training data, labeling, model training, deployment, and additional application logic to track movement.
The Study Guide explicitly associates Spatial Analysis with detecting the presence and movement of people in video under Implement computer vision solutions # Analyze videos .
질문 # 71
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 a 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：
The correct answer is D. Use real-time speech to text for incoming audio and text to speech for agent responses . The workflow requires continuous audio input, low-latency transcription for reasoning, and spoken output back to the user. Azure Speech in Foundry Tools real-time speech to text is designed for immediate transcription from streaming audio, which satisfies the incoming-audio side of the interaction. Text to speech provides the outbound spoken response path after the agent generates its answer.
This pattern aligns with Microsoft's real-time voice-agent architecture. The Voice Live API overview explains that low-latency speech-to-speech systems integrate speech recognition, generative reasoning, and text-to-speech functionality to create natural voice experiences. It also identifies contact centers as a key scenario and highlights low perceived latency for end users. Embeddings do not decode audio into conversational speech. Batch transcription introduces file-oriented delay and is not suitable for turn-taking.
Speech translation is only appropriate when translating between languages and does not provide the required reasoning-plus-spoken-response loop. Reference topics: Azure Speech in Foundry Tools, real-time speech to text, text to speech, voice agents, low-latency interaction, and conversational turn-taking.
질문 # 72
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal Al generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure protected material detection.
Does this meet the goal?
정답：A
설명：
The solution does not meet the goal. Protected material detection is intended to identify large language model output that matches known protected text or code, such as copyrighted text, selected web content, song lyrics, articles, recipes, or code. Microsoft describes protected material detection as a control for preventing AI- generated content from reproducing known protected material, not as a control for image safety or prompt injection.
The stated risk has two parts: users can upload unsafe images, and users can embed hidden instructions in images to manipulate the model. Unsafe image uploads require image moderation, because Azure AI Content Safety provides image APIs that detect harmful content across modalities and can support blocking decisions by harm category and severity. Hidden instructions extracted from images are indirect prompt injection or document attacks; Microsoft Prompt Shields are the capability designed to detect user prompt attacks and document attacks, including harmful instructions embedded in third-party content.
Therefore, protected material detection alone does not mitigate either primary risk. Reference topics: Azure AI Content Safety, image moderation, Prompt Shields, document attacks, indirect prompt injection, and protected material detection.
Topic 1, Case Study Contoso, Ltd
Overview
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative Al and agent- based solutions by using Microsoft Foundry.
Identity Environment:
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new Al engineering team named Agent1Dev Team to optimize and maintain existing Al solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement, monitor, and secure Al applications.
Contoso also has a team named Agent1Test Team that is responsible for validating Al solutions before the solution deployments.
Generative Environment:
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
* Agent1 uses a base model deployment.
* A safety evaluation pipeline is NOT enabled.
* Tool invocation approval workflows are NOT enabled.
* Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment:
Contoso stores product-related information in Azure resources that support Al applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statement:
Contoso identifies the following issues:
* Agent1 has only general knowledge of the Contoso products.
* A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
* Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
* The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirement:
Planned Changes:
Contoso plans to implement the following changes:
* Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
* Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
* Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
* Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
* Complete the development of the video creation solution.
Technical Requirements:
Contoso identifies the following technical requirements:
* The model deployment used by Agent1 must support scalable, high-throughput generative Al workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
* The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
* Responses generated by using the product sheet information must be relevant, complete, and accurate.
* Agent1 must be able to use the product sheets to answer natural language questions about product details.
* The model version used by Agent1 must remain consistent to ensure stable responses.
* The data processed by the model must remain within the EU.
Safety and Compliance Requirements:
Contoso identifies the following security and compliance requirements:
* API keys must NOT be used to access Foundry-deployed models.
* Access to the Azure resources must follow the principle of least privilege.
* The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
* Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
* Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
* Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
* The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Information:
Contoso identifies the following business requirements:
* Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
* Agent1 must answer questions only about the products sold by Contoso.
질문 # 73
You need to configure Agent1 to meet the security and compliance requirements.
What should you use?
정답：B
질문 # 74
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