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How to Prepare For AI-102: Designing and Implementing an Azure AI Solution Exam
Preparation Guide for AI-102: Designing and Implementing an Azure AI Solution Exam
Introduction
Microsoft has created a track for Azure professionals analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements, to get certified this platform. solution architects translate the vision and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets Microsoft's proficiency standards.
This certification been actually designed for aspirants design and implement AI apps and agents that use Microsoft Azure Cognitive Services.
For this exam candidate having proficiency in using cognitive service APIs to meet business requirements, appropriate AI models and services, automation requirements, data privacy and protection , bot state data , cognitive service output would be an added advantage.
Certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work as AI solution architect and prove his knowledge, certification offered by Microsoft. This AI-102 Exam Certification helps a candidate to validates his skills in Azure platform.
In this guide, we will cover the AI-102: Designing and Implementing an Azure AI Solution Certification exam, AI-102: Designing and Implementing an Azure AI Solution Certified professional salary and all aspects of the AI-102: Designing and Implementing an Azure AI Solution Certification.
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Prerequisites
Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Who should take the AI-102: Designing and Implementing an Azure AI Solution Exam
The AI-102 Exam certification is an internationally-recognized certification which help to have validation for Azure AI Solution Architects who have ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.
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Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Implement document intelligence solutions - Enrich and index data for search scenarios |
| Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Select appropriate Azure AI services - Manage security, compliance, and responsible AI |
| Implement Natural Language Processing Solutions | - Translate and interpret language using Azure AI services - Analyze text using Azure AI Language - Implement question answering and conversational AI |
| Implement Generative AI Solutions | - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions - Build retrieval-augmented generation (RAG) solutions |
| Implement Computer Vision Solutions | - Implement custom vision models - Extract information using OCR and form recognition - Analyze images and videos using Azure AI Vision |
Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) Sample Questions:
ユーザーがアップロードした複数のファイルを結合して処理するエージェントを開発する予定です。エージェントの開発にMicrosoft Foundry Agent Serviceを使用するかどうかを検討しています。このサービスにアップロードできるファイルの最大サイズはどれくらいですか？
- A. 1TB
- B. 10GB
- C. 300 GB
- D. 1GB
Correct Answer: C 🗳️
小売ドメインを使用して、会社の製品を識別するための Azure Custom Vision オブジェクト検出モデルをトレーニングします。
Android スマートフォン用のモバイル アプリの一部としてモデルを展開する予定です。
デプロイメント用にモデルを準備する必要があります。
順番に実行する必要がある 3 つのアクションはどれですか。回答するには、適切なアクションをアクション リストから回答領域に移動し、正しい順序に並べます。
Correct Answer:
Explanation:
For deployment to a mobile app (Android), Custom Vision requires a compact domain so the model can be exported and run on-device (e.g., as TensorFlow Lite or ONNX). The model was trained using the Retail domain, which is not an exportable compact domain for object detection. Therefore:
Change the model domain to an exportable Object Detection (compact) domain (e.g., General [compact]).
Retrain the model so weights are learned under the new domain-domain changes require retraining.
Export the model for Android (typically TensorFlow Lite or ONNX) and integrate it into the app.
This sequence prepares the model for on-device mobile deployment with minimal extra development.
References (Microsoft Azure AI Solution)
Custom Vision: Only compact domains support model export for edge/mobile.
Changing a domain requires retraining the iteration.
Exporting object detection models supports TensorFlow/TensorFlow Lite/ONNX formats for Android integration.
チャットボットで使用する会話フローを設計しています。
Microsoft Bot Framework エミュレーターを使用して会話フローをテストする必要があります。
.chat ファイルをどのように完成させるべきですか? 回答するには、回答エリアで適切なオプションを選択してください。
注意: 正しい選択ごとに 1 ポイントが付与されます。
Correct Answer:
Explanation:
Comprehensive Detailed Explanation along with All References available from Microsoft Azure AI Solution at the end of the explanation Typing: In a .chat transcript, you can simulate the bot's typing indicator with the Typing activity, optionally followed by a [Delay=...] . This matches the intent to pause before sending the next message.
AttachmentLayout=carousel: When sending multiple attachments (the two watch images), set the layout to carousel so the Emulator displays the attachments horizontally as a card carousel.
Adaptive Card attachment: The JSON file cards\watchProfileCard.json represents an Adaptive Card; specify adaptivecard so the Emulator renders the card.
Resulting .chat snippet (essentials):
user =User1
bot=watchbot
user : I want a new watch.
bot: [Typing][Delay= 3000 ]
bot: I can help you with that! Let me see what I can find.
bot: Her e ' s what I found.
bot:
[AttachmentLayout=carousel]
[Attachment=https://contoso.blob.core.windows.net/watch01.jpg]
[Attachment=https://contoso.blob.core.windows.net/watch02.jpg]
user: I like the first one.
bot: Sure, pulling up more information.
bot: [Attachment=cards\watchProfileCard.json adaptivecard]
user: That ' s nice! Thank you.
bot: Sure, you are most welcome!
References
Bot Framework Emulator transcript ( .chat ) activities and typing indicator: Bot Framework Activity schema; Emulator supports typing and delay in transcripts.
https://learn.microsoft.com/azure/bot-service/bot-service-debug-emulator
https://learn.microsoft.com/azure/bot-service/rest-api/bot-framework-rest-connector-api-reference#activity-object Attachment layout ( carousel / list ) for multiple attachments:
https://learn.microsoft.com/azure/bot-service/nodejs/bot-builder-nodejs-send-rich-cards#carousel-layout Adaptive Cards in Bot Framework:
https://learn.microsoft.com/adaptive-cards/get-started/bots
https://learn.microsoft.com/azure/bot-service/bot-builder-howto-add-media-attachments#adaptive-cards
遠隔地の学習者向けにインターネットベースのトレーニング ソリューションを開発しています。
あなたの会社では、研修中に、一部の受講者が長時間机を離れたり、気が散ったりしていることがわかりました。
学習者が授業に出席し、集中しているかどうかを検知するには、各学習者のコンピュータから送信されるビデオと音声のフィードを使用する必要があります。ソリューションは、開発の労力を最小限に抑え、各学習者を識別できるものでなければなりません。
各要件に応じてどの Azure Cognitive Services サービスを使用する必要がありますか? 回答するには、回答領域で適切なオプションを選択してください。
注意: 正しい選択ごとに 1 ポイントが付与されます。
Correct Answer:
Explanation:
Scenario Recap
You are building a remote training monitoring solution.
Requirement: Use video and audio feeds to detect if a learner is present, paying attention, and talking.
Services available: Face, Speech, Text Analytics.
Analysis
From a learner's video feed, verify whether the learner is present.
The Face API can detect and identify faces in a video feed.
It can tell if a person is present and recognized, fulfilling the requirement.
From a learner's facial expression in the video feed, verify whether the learner is paying attention.
Again, the Face API provides facial expression and emotion recognition (happiness, anger, neutral, etc.).
This can be mapped to "paying attention vs. distracted."
From a learner's audio feed, detect whether the learner is talking.
The Speech service detects spoken input and can determine if speech is present.
Text Analytics works on text (not raw audio) and is therefore not appropriate here.
Final Answer (Answer Area Selections)
From a learner's video feed, verify whether the learner is present: Face From a learner's facial expression in the video feed, verify whether the learner is paying attention: Face From a learner's audio feed, detect whether the learner is talking: Speech Microsoft References Face API - Face detection & identification Face API - Emotion recognition Azure Speech service
自然言語モデルを構築しています。
アクティブラーニングを有効にする必要があります。
何をすべきでしょうか?
- A. 音声プライミングを有効にします。
- B. 予測エンドポイント クエリに show-all-intents=true を追加します。
- C. 感情分析を有効にします。
- D. 予測エンドポイント クエリに log=true を追加します。
Correct Answer: D 🗳️
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