AI-103のMicrosoft試験問題の高い合格率は98％〜100％であるため、正確かつ最新のAI-103試験トレントで市場に並ぶものがないと誇らしげに主張できます。 成功をもたらす当社の強みと、取得する意図のある認定を疑うことはありません。 AI-103実践教材を使用して、候補者の勝利をますます証明しています。Xhs1991 あなたは彼らのような勝者の一人になると信じています。 Developing AI Apps and Agents on AzureのAI-103学習教材を購入するだけで、より明るい未来を手にすることができます。
| Section | Weight | Objectives |
|---|---|---|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
我々の目標はAI-103試験を準備するあなたにヘルプを提供してあなたに試験に合格させることです。この目標を達成するために、我々Xhs1991は時間とともに迅速に発展しています。今まで精確的な問題集を開発しています。我々のAI-103問題集を利用しているあなたは一発で試験に合格できると信じています。心配なく我々の資料を利用してください。
質問 # 33
You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
* Multiple client applications must use the same search configuration.
* A security policy must prevent key-based authentication.
* Administrative effort must be minimized.
What should you do?
正解：C
解説：
The correct solution is to configure an Azure AI Search connection in Project1 and reference that connection from each application. Microsoft Foundry project connections are intended to centralize external resource configuration for a project. The official Foundry guidance states that when adding a connection, you select the external service, such as Azure AI Search , and choose the authentication method for that resource.
This directly satisfies the requirement for multiple client applications to use the same search configuration instead of duplicating endpoints, indexes, and credentials in each app.
The security requirement is met by configuring the connection with keyless authentication. The Azure AI Search integration guidance specifies that a Foundry project connection requires the search endpoint and either key-based authentication or keyless authentication with Microsoft Entra ID . For keyless authentication, RBAC roles are assigned to the project's managed identity, eliminating hard-coded API keys.
Options A and C use secure identity-based access but force every client application to maintain its own Azure AI Search configuration, increasing administration and violating the shared-configuration requirement. Option B also increases administrative work and does not use the native Azure AI Search connection type. Reference topics: Microsoft Foundry project connections, Azure AI Search grounding, Microsoft Entra ID authentication, managed identities, and RBAC.
質問 # 34
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?
正解：B
解説：
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.
質問 # 35
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project. User messages can contain both text and images. Currently, App1 includes image URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.
正解：B
質問 # 36
You are building an app that will share user images. You need to configure the app to:
* Categorize each image as a photograph or drawing.
* Generate a caption for the image.
* Minimize development effort.
Which two services should you include?
正解：D、E
解説：
Use image type detection to distinguish drawings from ordinary photographic content. Azure Vision's ImageType visual feature returns information including lineDrawingType, which indicates whether the submitted image is a line drawing, and clipArtType, which estimates whether it is clip art. The application can use these pretrained results to implement the required photograph-versus-drawing categorization without collecting labeled images or training a custom classifier.
Use image descriptions to generate the caption. Azure Vision Image Analysis provides the Caption visual feature, which generates a concise, one-sentence description of the overall image. Dense Captions can additionally describe individual regions, but the standard Caption feature directly satisfies the stated requirement.
Object detection identifies and locates individual objects by using bounding boxes; it does not determine whether the entire image is a photograph or drawing. Content tags produce descriptive keywords rather than a natural-language caption. Azure Custom Vision classification could be trained for the image-type distinction, but it would require image collection, labeling, training, evaluation, and deployment, increasing development effort unnecessarily.
Study Guide alignment: configure applications to produce captions and implement solutions that identify visual characteristics within images .
質問 # 37
Hotspot Question
You are building a model to detect objects in images.
The performance of the model based on training data is shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: 0
The percentage of false positives is 0%.
Because the model made zero incorrect positive predictions, the count and percentage of false positives must be exactly zero.
Box 2: 25
The value for the number of true positives divided by the total number of true positives and false negatives is 25% (or 0.25).
High Precision (100%): Every single object your model detects is correct; it generates zero false alarms.
Low Recall (25%): Your model misses 75% of the actual objects it was supposed to find.
'
Reference:
https://medium.com/grabngoinfo/how-to-evaluate-the-performance-of-a-binary-classification-model-6e7193dcbbf9
質問 # 38
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当社Xhs1991のソフトウェアを練習するには20〜30時間しかかからず、試験に参加できます。 AI-103学習の質問を学ぶのに時間を費やす必要はありません。また、毎日AI-103ガイド急流を学ぶのに数時間しかかかりません。 AI-103試験の質問は効率的であり、AI-103試験に簡単に合格できることを保証できます。 しかし、当社のAI-103試験トレントを購入すると、時間と労力を節約でき、他のことをするための時間を節約できます。
AI-103最新受験攻略: https://www.xhs1991.com/AI-103.html