さらに、GoShiken 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=1WQmaUgxHopuOMCm0o2e8K3LlSFjyDZSY
世界は変化している、我々はできるだけそのペースを維持する必要があります。我々GoShikenはMicrosoftのAI-102試験の変化を注目しています。数年以来の試験問題集を研究しています。現在あなたに提供するのは大切なMicrosoftのAI-102資料です。あなたの購入してから、我々はあなたにMicrosoftのAI-102資料の更新情況をつど提供します。このサービスは無料なのです。あなたが我々の資料を購入するとき、あなたのMicrosoftのAI-102試験に関するすべてのヘルプを購入しました。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Process and index video content - Build and deploy custom vision models - Integrate vision capabilities into applications - Extract text and handwriting from images - Analyze images and detect objects/features |
| Topic 2: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models |
| Topic 3: Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Select suitable AI models - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services |
| Topic 4: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases |
| Topic 5: Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback |
| Topic 6: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration |
もう既にMicrosoftのAI-102認定試験を申し込みましたか。「もうすぐ試験の時間なのに、まだ試験に合格する自信を持っていないですが、どうしたらいいでしょうか。何か試験に合格するショートカットがあるのですか。試験参考書を読み終わる時間も足りないですから・・・」いまこのような気持ちがありますか。そうしても焦らないでくださいよ。試験を目前に控えても、ちゃんと試験に準備するチャンスもあります。何のチャンスですかと聞きたいでしょう。それはGoShikenのAI-102問題集です。これは効果的な資料で、あなたを短時間で試験に十分に準備させることができます。この問題集の的中率がとても高いですから、問題集に出るすべての問題と回答を覚える限り、AI-102認定試験に合格することができます。
質問 # 335
You build a custom Form Recognizer model.
You receive sample files to use for training the model as shown in the following table.
Which three files can you use to train the model? Each correct answer presents a complete solution. (Choose three.) NOTE: Each correct selection is worth one point.
正解：A、C、F
解説：
Explanation
Input requirements
Form Recognizer works on input documents that meet these requirements:
Format must be JPG, PNG, PDF (text or scanned), or TIFF. Text-embedded PDFs are best because there's no possibility of error in character extraction and location.
File size must be less than 50 MB.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer/overview
質問 # 336
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You build a language model by using a Language Understanding service. The language model is used to search for information on a contact list by using an intent named FindContact.
A conversational expert provides you with the following list of phrases to use for training.
Find contacts in London. Who do I know in Seattle?
Search for contacts in Ukraine.
You need to implement the phrase list in Language Understanding.
Solution: You create a new intent for location.
Does this meet the goal?
正解：B
解説：
The scenario involves a Language Understanding (LUIS) or Conversational Language Understanding (CLU) model that has:
* An intent called FindContact (used for searching a contact list).
* Training phrases like:
* "Find contacts in London"
* "Who do I know in Seattle?"
* "Search for contacts in Ukraine"
The model must recognize locations (London, Seattle, Ukraine) inside the FindContact intent.
* The proposed solution is to create a new intent for location.
* This is not correct because:
* Intents represent what the user wants to do (the action, e.g., FindContact).
* Entities represent details within the user's utterance (the data, e.g., location).
* Here, the intent is already defined: FindContact.
* Location should be modeled as an entity (Location entity), not as a separate intent.
Creating a new intent for each location (London, Seattle, Ukraine) would fragment the model and make it harder to generalize. Instead, the model should capture location as a parameter of the single intent FindContact.
* Keep the existing FindContact intent.
* Add an entity for location (prebuilt GeographyV2 in LUIS or "Location" entity in CLU).
* Train the model so that London, Seattle, Ukraine are recognized as entity values inside the FindContact intent.
The answer: B. No
* Intents and entities in Language Understanding
* Prebuilt entity: GeographyV2
質問 # 337
You are developing the smart e-commerce project.
You need to design the skillset to include the contents of PDFs in searches.
How should you complete the skillset design diagram? To answer, drag the appropriate services to the correct stages. Each service may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: Azure Blob storage
At the start of the pipeline, you have unstructured text or non-text content (such as images, scanned documents, or JPEG files). Data must exist in an Azure data storage service that can be accessed by an indexer.
Box 2: Computer Vision API
Scenario: Provide users with the ability to search insight gained from the images, manuals, and videos associated with the products.
The Computer Vision Read API is Azure's latest OCR technology (learn what's new) that extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents.
Box 3: Translator API
Scenario: Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.
Box 4: Azure Files
Scenario: Store all raw insight data that was generated, so the data can be processed later.
Reference:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-concept-intro
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
質問 # 338
Hotspot Question
You have an Azure subscription that contains an Azure AI Content Safety resource named CS1.
You need to use the SDK to call CS1 to identify requests that contain harmful content.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/use- blocklist?tabs=windows%2Crest
質問 # 339
You have a Language Understanding solution that runs in a Docker container.
You download the Language Understanding container image from the Microsoft Container Registry (MCR).
You need to deploy the container image to a host computer.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
正解：
解説：
1 - From the Language Understanding portal, export...
2 - From the host computer, move the packgae file...
3 - From the computer, build the container and....
質問 # 340
......
GoShikenの実践教材は、学生だけでなくオフィスワーカーにも適用されます。 職場の退役軍人だけでなく、新しく採用された新人にも適用されます。 AI-102の学習教材は、非常にシンプルで理解しやすい言語を使用して、すべての人が学習して理解できるようにします。 また、AI-102の実際のテストでは、教科書を読むのがつまらないことを回避できますが、Microsoft演習を行う過程で重要な知識をすべて習得できます。 そして、AI-102試験問題の高い合格率は98％以上です。 Designing and Implementing a Microsoft Azure AI Solution学習ガイドを試してみませんか？
AI-102日本語資格取得: https://www.goshiken.com/Microsoft/AI-102-mondaishu.html
ちなみに、GoShiken 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=1WQmaUgxHopuOMCm0o2e8K3LlSFjyDZSY