ちなみに、CertShiken AI-102の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1brYKp6rDUxV6aUeusgBbLTS_IRdpKh4L
AI-102試験はCertShikenの教材を準備し、高品質で合格率が高く、実際の試験を十分に理解しており、AI-102学習教材を長年にわたって作成した専門家によって完了します。彼らは、AI-102試験の準備をするときに受験者が本当に必要とするものを非常によく知っています。また、実際のAI-102試験の状況を非常によく理解しています。実際の試験がどのようなものかをお知らせします。AI-102試験問題のソフトバージョンを試すことができます。これにより、実際の試験をシミュレートできます。
| Section | Weight | Objectives |
|---|---|---|
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Extract text and handwriting from images - Process and index video content - Build and deploy custom vision models - Integrate vision capabilities into applications |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions |
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Implement model monitoring and feedback - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning |
| Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots |
私たちMicrosoftのAI-102トレントは、紙で学ぶだけでなく、携帯電話を使って学習できるように、さまざまなバージョンを特別に提案しました。 これにより、生徒が断片化した時間を利用できるようになります。 興味や習慣に応じて、CertShikenのAI-102学習教材のバージョンを選択できます。 バリューパックを購入すると、3つのバージョンがすべて揃っており、価格は非常に優遇されており、すべての学習体験を楽しむことができます。 つまり、いつでもどこでもAI-102試験エンジンを勉強して、Designing and Implementing a Microsoft Azure AI Solution試験に合格するのに役立ちます。
質問 # 156
You are developing a solution for the Management-Bookkeepers group to meet the document processing requirements. The solution must contain the following components:
A From Recognizer resource
An Azure web app that hosts the Form Recognizer sample labeling tool
The Management-Bookkeepers group needs to create a custom table extractor by using the sample labeling tool.
Which three actions should the Management-Bookkeepers group perform in sequence? To answer, move the appropriate cmdlets from the list of cmdlets to the answer area and arrange them in the correct order.
正解：
解説：
Explanation
Step 1: Create a new project and load sample documents
Create a new project. Projects store your configurations and settings.
Step 2: Label the sample documents
When you create or open a project, the main tag editor window opens.
Step 3: Train a custom model.
Finally, train a custom model.
Reference:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/label-tool
質問 # 157
You are building an app that will include one million scanned magazine articles. Each article will be stored as an image file. You need to configure the app to extract text from the images. The solution must minimize development effort. What should you include in the solution?
正解：D
解説：
The requirement is to extract text from scanned images (OCR at scale).
Computer Vision Image Analysis can describe and tag images but is not optimized for bulk OCR.
Form Recognizer is intended for structured document extraction (invoices, receipts, forms), not raw magazine scans.
Azure Cognitive Service for Language works only with text, not images.
Read API in Computer Vision is specifically designed for extracting text from scanned images, supports multiple languages, and is ideal for this scenario.
質問 # 158
You are building an Azure Weblob that will create knowledge bases from an array of URLs.
You instantiate a QnAMakerClient object that has the relevant API keys and assign the object to a variable named client.
You need to develop a method to create the knowledge bases.
Which two actions should you include in the method? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：A、D
解説：
Comprehensive Detailed ExplanationThe scenario:
* You are building an Azure WebJob to create knowledge bases from an array of URLs.
* You already have a QnAMakerClient object instantiated with the relevant API keys.
* You need to know what steps are required to create a knowledge base programmatically.
* CreateKbDTO is the request payload for creating a knowledge base.
* It contains the name of the knowledge base and its sources, which may include:
* A list of URLs.
* A list of QnA pairs (QnADTO).
* A list of files (FileDTO).
* In this case, since the WebJob provides URLs, they will be passed inside the CreateKbDTO.Sources property.
* Once the CreateKbDTO object is defined, you use the QnAMakerClient to send the request:
* var response = await client.Knowledgebase.CreateAsync(createKbDTO);
* This triggers the asynchronous creation of a new knowledge base in the QnA Maker service.
* A. Create a list of FileDTO objects: Not required here, because the question specifies URLs, not files.
* C. Create a list of QnADTO objects: Also not needed, since you're not directly supplying QnA pairs; the knowledge base will be built from URLs.
Step 1 - Create a CreateKbDTO objectStep 2 - Call client.Knowledgebase.CreateAsyncWhy not A or C?
Correct AnswerB. Call the client.Knowledgebase.CreateAsync method.
D). Create a CreateKbDTO object.
* QnA Maker - Create Knowledge Base API
* QnAMakerClient Class - Azure SDK for .NET
* CreateKbDTO Class
Microsoft References
質問 # 159
You are developing a solution for the Management-Bookkeepers group to meet the document processing requirements. The solution must contain the following components:
A From Recognizer resource
An Azure web app that hosts the Form Recognizer sample labeling tool
The Management-Bookkeepers group needs to create a custom table extractor by using the sample labeling tool.
Which three actions should the Management-Bookkeepers group perform in sequence? To answer, move the appropriate cmdlets from the list of cmdlets to the answer area and arrange them in the correct order.
正解：
解説：
Explanation
Step 1: Create a new project and load sample documents
Create a new project. Projects store your configurations and settings.
Step 2: Label the sample documents
When you create or open a project, the main tag editor window opens.
Step 3: Train a custom model.
Finally, train a custom model.
Reference:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/label-tool
質問 # 160
You need to create a new resource that will be used to perform sentiment analysis and optical character recognition (OCR). The solution must meet the following requirements:
Use a single key and endpoint to access multiple services.
Consolidate billing for future services that you might use.
Support the use of Computer Vision in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
Box 1: PUT
Sample Request: PUT
https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/test-rg/ Reference:
https://docs.microsoft.com/en-us/rest/api/deviceupdate/resourcemanager/accounts/create
https://www.analyticsvidhya.com/blog/2020/12/microsoft-azure-cognitive-services-api-for-ai-development/
質問 # 161
......
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AI-102模擬対策: https://www.certshiken.com/AI-102-shiken.html
さらに、CertShiken AI-102ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1brYKp6rDUxV6aUeusgBbLTS_IRdpKh4L