最近のレポートによると、複数のスキル証明書を所有している人は、上司によって昇格されやすくなっています。日常から離れて理想的な生活を求めるには、職場で高い得点を獲得し、試合に勝つために余分なスキルを習得しなければなりません。 AI-103試験問題は、あなたの夢をかなえるのに役立ちます。さらに、AI-103ガイドトレントに関する詳細情報を提供するWebサイトにアクセスできます。 AI-103試験問題を試してみてください。そうすれば、AI-103試験に合格できることがわかります。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 2: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 3: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 4: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 5: Implement agentic solutions | 20-25% | - Build AI agents
|
MicrosoftのAI-103試験はIT領域で人気がある重要な試験です。我々はIT領域の人々にショートカットを提供するために、最高のスタディガイドと最高のオンラインサービスを用意して差し上げます。Jpexamの MicrosoftのAI-103試験問題集は全ての試験の内容と答案に含まれています。Jpexamの模擬テストを利用したら、これはあなたがずっと全力を尽くてもらいたいもののことが分かって、しかもそれは正に試験の準備をすることを意識します。
質問 # 64
You plan to deploy a containerized version of an Azure Al Language service that will be used for sentiment analysis.
You configure https: //contoso.cognitiveservices.azure, cow as the endpoint URI for the service.
You need to run the container on an Azure virtual machine by using Docker.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
First selection:
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment
Second selection:
https://contoso.cognitiveservices.azure.com
Completed command:
docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment \
Eula=accept \
Billing=https://contoso.cognitiveservices.azure.com \
ApiKey=xxx
The image mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment is the official Microsoft Container Registry image for Azure AI Language sentiment analysis. The Billing argument must reference the endpoint of the provisioned Azure AI Language resource associated with the supplied API key. It is not the address used by applications to invoke the locally running container.
Azure
AI containers require three licensing and metering parameters: Eula=accept, a valid Billing endpoint, and ApiKey. Without all three values, the container does not start. The command maps host port 5000 to container port 5000, allocates 8 GB of memory and one CPU, creates an interactive terminal, and automatically removes the stopped container. Applications subsequently invoke the locally hosted service through an address such as http://
< vm-address > :5000. The Azure endpoint remains responsible for metering and authentication; analyzed customer text is processed by the container rather than sent to the billing endpoint.
Study Guide alignment: Implement text analysis solutions - perform sentiment analysis and opinion mining, deploy Azure Language containers, and configure container billing parameters.
質問 # 65
You have a large collection of image files and PDF documents stored in an Azure Storage account. The documents contain tabular data.
You need to extract the tables into a structured format that can be imported into a database. The solution must minimize development effort. What should you use?
正解：C
解説：
To extract tables from numerous images and PDF documents in Azure Storage into a structured database with low development, you should utilize Azure AI Document Intelligence (formerly Form Recognizer) within the Azure AI Foundry ecosystem. The Layout Model is best for extracting tables, rows, columns, and headers.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/tools/document-intelligence
質問 # 66
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 # 67
You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per- field confidence score and source grounding to locations within the source document.
What should you do?
正解：B
解説：
To fulfill all your requirements using Azure Content Understanding in Foundry Tools, you need to configure a custom document analyzer with specific flags, set up an index ingestion pipeline, and build a downstream human-in-the-loop validation rule.
*-> 1. Enable Confidence Scores and Source Grounding
To force the analyzer to provide per-field confidence metrics and precise layout/bounding box coordinates for verification, you must opt-in to the estimate FieldSourceAndConfidence parameter within your configuration.
Option A (Global): Set estimateFieldSourceAndConfidence = true in the main analyzer config to evaluate all fields.
Option B (Field-Level): Set estimateSourceAndConfidence = true under individual field schemas.This ensures the generated JSON response populates the bounding box coordinates, page numbers, and a confidence score 0.0 to 1.0 for every extracted entity.
2. Configure Document Extraction for Two-Column & Table Layouts
3. Build the Ingestion Pipeline (RAG vs. Automation Dual-Path)
4. Implement Threshold Routing and Source Verification
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/overview
質問 # 68
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
- Ensure that background objects can be removed by applying a mask-
based inpainting edit.
- Preserve the original lighting and style of the edited images.
- Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
正解：C
質問 # 69
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AI-103の科学技術の改善は、社会の将来の建設と進歩に計り知れない力を生み出します。 AI-103模擬試験は、緊急の課題に対処するための最適な選択および有用なツールとなります。 10年以上の努力により、当社のAI-103トレーニング資料は、業界で最も広く称賛され、待望の製品になりました。 AI-103模擬試験の計画と設計において、プロのエリートから完全な技術サポートを受けています。もうheしないでください。 AI-103学習エンジンの購入を後悔することはありません！
AI-103復習対策書: https://www.jpexam.com/AI-103_exam.html