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Microsoft AI-102 Exam is an advanced-level certification that requires a deep understanding of AI technologies and their applications. Candidates are expected to have a strong grasp of AI concepts, programming languages, and data science principles. Passing AI-102J exam is a great way to demonstrate your expertise in designing and implementing AI solutions using Microsoft Azure, and it can help you advance your career in the field of AI and machine learning.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Topics Covered
Exam AI-102 contains five topics each of which is intended to check specific skills.
1. Plan and Manage an Azure Cognitive Services Solution
This topic implies your ability to choose the suitable Cognitive Services resource, create it, plan and design security for a Cognitive Services solution, and apply Cognitive Services containers. This means that you should be competent in selecting the appropriate cognitive service for solutions that refer to language analysis, speech, decision support, and vision. You also should possess skills to operate costs of Cognitive Services, create a Cognitive Services resource, and monitor a cognitive service. This part also checks how well you can operate Cognitive Services account keys, and protect Cognitive Services. Your knowledge of using Face API, Computer Vision, Speech, Text Analysis, and ability to integrate Cognitive Services Containers in Microsoft Azure will also be assessed.
2. Implement Computer Vision Solutions
The second topic is designed to check your skills in using the Computer Vision API to get image descriptions, define landmarks, find brands, edit content in images, and create thumbnails. In this part, you are expected to be able to detect faces and recognize them in images, analyze facial features, and match similar faces with the help of the Face API. Being competent in utilizing the Custom Vision service, you should demonstrate your skills in applying image classification and implementing an object detection solution. Besides, your ability to analyze video by implementing Azure Video Analyzer for Media will be measured.
3. Implement Natural Language Processing Solutions
In the third topic, candidates are required to show their skills in analyzing text by utilizing the Text Analytics service, control speech by implementing the Speech service, translate the text with the help of the Translator service. This domain also checks your proficiency in creating and optimizing an initial language model by utilizing LUIS, and finally, managing it.
4. Implement Knowledge Mining Solutions
In this domain, you will be required to have expertise related to applying a Cognitive Search solution, which implies creating data sources, identifying an index, running an indexer, and using synonyms. This topic also aims to evaluate your ability to apply an enrichment pipeline, use a knowledge store, operate a Cognitive Search solution and indexing.
5. Implement Conversational AI Solutions
This domain will evaluate your capacity in utilizing QnA Maker to make a knowledge base, creating and implementing conversation flow, creating a bot by utilizing either the Bot Framework Composer or the Bot Framework SDK. Finally, you will need to demonstrate your skills in integrating Cognitive Services into a bot.
Microsoft AI-102 certification exam is an essential certification for professionals who want to demonstrate their expertise in developing and implementing AI solutions in the Azure environment. By passing AI-102J exam, you will be able to showcase your skills and knowledge to potential employers and enhance your career prospects in the field of AI.
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection |
| Topic 2: Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Extract text and handwriting from images - Integrate vision capabilities into applications - Analyze images and detect objects/features - Process and index video content |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes |
| Topic 4: Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types |
| Topic 5: Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Orchestrate multiple models and containers |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles - Select suitable AI models |
Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) Sample Questions:
QA1 という名前の Azure Al Language カスタム質問応答プロジェクトが含まれる Azure サブスクリプションがあります。
質問と回答のペアを QA1 にインポートする必要があります。
どの 2 つのファイル形式を使用できますか? それぞれの正解は完全なソリューションを示します。注: 正解の選択ごとに 1 ポイントが与えられます。
- A. エクセル
- B. CSV
- C. TSV
- D. JSON
- E. 読み取り
Correct Answer: A,C 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
管理会計グループ向けのドキュメント処理要件を満たすソリューションを開発しています。ソリューションには以下のコンポーネントが含まれている必要があります。
フォーム認識リソース
Form Recognizer サンプル ラベリング ツールをホストする Azure Web アプリ
Management-Bookkeepers グループは、サンプル ラベル付けツールを使用してカスタム テーブル抽出プログラムを作成する必要があります。
Management-Bookkeepers グループは、どの 3 つのアクションを順番に実行する必要がありますか? 回答するには、適切なコマンドレットをコマンドレットの一覧から回答領域に移動し、正しい順序に並べます。
Correct Answer:
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
Search1 という名前の Azure Al Search リソースがあります。
Search1 を使用してコンテンツのインデックスを作成する App1 という名前のアプリがあります。
アプリが Search1 を使用して請求書のプロパティを認識して取得できるようにするには、App1 にカスタム スキルを追加する必要があります。
ソリューションには何を含めるべきですか?
- A. Azure AI ドキュメント インテリジェンス
- B. Azure OpenAI
- C. Azure Al イマーシブ リーダー
- D. Azure カスタム ビジョン
Correct Answer: A 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
OpenAI1 という名前の Azure OpenAI リソースと User1 という名前のユーザーを含む Azure サブスクリプションがあります。
User1 が OpenA11 にデータセットをアップロードし、既存のモデルを微調整できるようにする必要があります。このソリューションは、最小権限の原則に従う必要があります。
ユーザーにどのロールを割り当てる必要がありますか?
- A. Cognitive Services OpenAI ユーザー
- B. Cognitive Services 貢献者
- C. Cognitive Services OpenAI 貢献者
- D. 貢献者
Correct Answer: C 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
テキスト処理ソリューションを開発しています。
以下に示す関数があります。
2 番目の引数では、関数を呼び出して次の文字列を指定します。
私たちのパリ旅行にはエッフェル塔の訪問も含まれていました。
以下の各文について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」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:
The output will include the following words: our and included. No
The output will include the following words: Paris, Eiffel, and Tower. Yes The function will output all the key phrases from the input string to the console. No The output will include the following words: our and included. No The output will include the following words: Paris, Eiffel, and Tower. Yes The function will output all the key phrases from the input string to the console. No The function uses RecognizeEntities to identify entities in the text, which typically includes named entities like locations (e.g., Paris, Eiffel Tower). It then prints the text of each recognized entity. The input string " Our tour of Paris included a visit to the Eiffel Tower " contains the entities " Paris " and " Eiffel Tower, " which will be broken down into individual words like " Paris, " " Eiffel, " and " Tower. " However, common words like " our " and " included " are not typically recognized as entities, and the function does not output all key phrases, only the recognized entities.
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