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How to book the AI-102: Designing and Implementing an Azure AI Solution Exam
These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
- Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
- Step 2: Sign up/Login to Pearson VUE account
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Schedule Exam | Pearson VUE |
| Number of Questions | 40-60 |
| Exam Code | AI-102 |
| Passing Score | 700 / 1000 |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Duration | 130 mins |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Exam Price | $165 (USD) |
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Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.
They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.
Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.
They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.
Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate
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Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models |
| Topic 2: Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select suitable AI models - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions |
| Topic 3: 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 |
| Topic 4: 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 |
| Topic 5: Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Process and index video content - Integrate vision capabilities into applications - Extract text and handwriting from images - Analyze images and detect objects/features |
| Topic 6: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models |
Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) Sample Questions:
Foundry コントロール プレーン リソースのコンテンツ セーフティを含む Azure サブスクリプションがあります。
ソーシャル メディア メッセージング アプリを構築しています。
メッセージを分析し、不適切な内容を含むメッセージにフラグを立てるソリューションを構築する必要があります。
コード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 ポイントが加算されます。
Correct Answer:
Explanation:
テスト用ローカル デバイスとオンプレミス データセンターで、コンテナー化されたバージョンの Anomaly Detector API を使用する予定です。
コンテナ化されたデプロイメントが次の要件を満たしていることを確認する必要があります。
コンテナを実行するデバイスのコマンドライン履歴に課金情報や API 情報が保存されないようにします。
Azure ロールベースのアクセス制御 (Azure RBAC) を使用して、コンテナー イメージへのアクセスを制御します。
どの4つのアクションを順番に実行すべきでしょうか？ 解答するには、アクションリストから適切なアクションを解答エリアに移動し、正しい順序に並べてください。（4つ選択してください。）注：正解の選択肢は複数あります。正解の順序を選択した場合でも、得点は加算されます。
Correct Answer:
Explanation:
Comprehensive Detailed Explanation
You are deploying Anomaly Detector API as a container. The key requirements are:
Prevent billing and API keys from being exposed in command-line history.
Control access to container images using Azure RBAC.
Step-by-step reasoning:
Pull the Anomaly Detector container image
Microsoft Cognitive Services (including Anomaly Detector) provides container images via Microsoft Container Registry.
You must first pull the container image locally.
Push the image to an Azure container registry (ACR)
To enforce Azure RBAC, you need to use Azure Container Registry instead of Docker Hub.
Storing in ACR ensures authentication and role-based access control to the image.
Distribute a docker run script
Instead of providing credentials directly on the command line (which risks storing API keys in shell history), best practice is to distribute a docker run script (or use environment variables / mounted files) to run the container.
This prevents exposure of sensitive information.
Run the container on local devices or on-premises datacenters
The container now runs securely, with API key and billing info injected at runtime via environment variables or mounted secrets.
Incorrect Options
Create a custom Dockerfile # Not needed, because Microsoft provides the official Anomaly Detector container image.
Build the image # Not required; again, the container is prebuilt.
Push the image to Docker Hub # Does not allow RBAC, so this would not meet the security requirement.
Correct Sequence:
Pull the Anomaly Detector container image.
Push the image to an Azure container registry.
Distribute a docker run script.
Run the container in the local or on-prem environment.
Microsoft References
Use Cognitive Services containers
Azure Container Registry - Authentication and RBAC
Best practices for running Cognitive Services containers
それぞれ独自の言語理解モデルを持つチャットボットが 100 個あります。
多くの場合、各モデルに同じフレーズを追加する必要があります。
新しいフレーズを含めるには、言語理解モデルをプログラムで更新する必要があります。
コードはどのように完成させるべきでしょうか？答えは、適切な値を正しいターゲットにドラッグすることです。各値は1回、複数回、または全く使用されない場合があります。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要がある場合があります。
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:
Box 1: AddPhraseListAsync
Example: Add phraselist feature
var phraselistId = await client.Features.AddPhraseListAsync(appId, versionId, new PhraselistCreateObject
{
EnabledForAllModels = false,
IsExchangeable = true,
Name = " QuantityPhraselist " ,
Phrases = " few,more,extra "
});
Box 2: PhraselistCreateObject
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/client-libraries-rest-api
Azure AI Language サービスを使用して機密性の高い顧客データを処理するソリューションを Azure で構築しています。
特定のAzureプロセスのみが言語サービスにアクセスできるようにする必要があります。このソリューションでは、管理作業を最小限に抑える必要があります。
ソリューションには何を含めるべきですか?
- A. 仮想ネットワークルール
- B. 仮想ネットワークゲートウェイ
- C. IPsecルール
- D. Azure アプリケーション ゲートウェイ
Correct Answer: A 🗳️
Explanation: Only visible for DumpTorrent members. You can sign-up / login (it's free).
Azureサブスクリプションをお持ちです。このサブスクリプションには、ModellというGPT-4モデルとApplというアプリをホストするAzure OpenAIリソースが含まれています。App1はModel1を使用します。
App1 がヘイトスピーチを含む回答を返さないようにする必要があります。
Model1 には何を設定する必要がありますか?
- A. 虐待監視
- B. 温度パラメータ
- C. コンテンツフィルタ
- D. 頻度ペナルティパラメータ
Correct Answer: C 🗳️
Explanation: Only visible for DumpTorrent members. You can sign-up / login (it's free).
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