How much AI-102: Designing and Implementing an Azure AI Solution Exam Cost
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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AI-102: Designing and Implementing an Azure AI Solution Exam Certified Professional salary
The average salary of a AI-102: Designing and Implementing an Azure AI Solution Exam Certified Expert in
- United State - 120,000 USD
- Europe - 90,547 EURO
- England - 90,532 POUND
- India - 20,00,327 INR
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Deploy and manage generative models - Apply prompt engineering and fine-tuning |
| Topic 2: Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization |
| Topic 3: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select suitable AI models - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources |
| Topic 4: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval |
| Topic 5: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration - Understand agent use cases and types |
| Topic 6: Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Extract text and handwriting from images - Analyze images and detect objects/features - Build and deploy custom vision models - Process and index video content |
Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) Sample Questions:
Foundry ToolsのAzure Speechを使用して音声ファイルを翻訳するアプリを作成する必要があります。コードをどのように完成させればよいでしょうか？回答するには、回答欄で適切なオプションを選択してください。
注：正解ごとに1ポイントが加算されます。
Correct Answer:
Explanation:
あなたの会社には、レポートをページングした植え替えソリューションがあります。レポートは、データウェアハウス内のディメンションモデルをクエリします。レポートソリューションはどのタイプの処理を使用しますか？
- A. オンライン分析処理（OLAP）
- B. オンライントランザクション処理（OLTP）
- C. ストリーム処理
- D. バッチ処理
Correct Answer: A 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
言語翻訳を含むアプリケーションを開発しています。
アプリケーションは、get_text_to_be_translated 関数を使用して取得したテキストを翻訳します。テキストは複数の言語から選択できます。テキストの内容は、Azure の地域（アメリカ大陸）に限定する必要があります。
テキストを単一の言語に翻訳するコードを開発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 ポイントが付与されます。
Correct Answer:
Explanation:
API Endpoint Selection
The constraint states that the content of the text must remain within the Americas Azure geography .
" api.cognitive.microsofttranslator.com " : This is the global endpoint , which doesn ' t guarantee data residency in a specific geography.
" api-apc.cognitive.microsofttranslator.com " : This endpoint refers to the Asia Pacific geography .
" api-nam.cognitive.microsofttranslator.com " : This endpoint refers to the North and South America (NAM) geography .
Therefore, to meet the data residency requirement for the Americas, you must select " api-nam.cognitive.
microsofttranslator.com " .
2. Path/Query String Selection
The requirement is to translate the text to a single language . The input text can be in " one of many languages, " meaning the source language isn ' t fixed and should be detected automatically. The language to translate to is implied to be English ( en ) based on the options available and common translation scenarios.
" /translate?from=en " : This is incorrect because it specifies the source language as English, which contradicts the " text can be in one of many languages " requirement (implying automatic source language detection).
" /translate?suggestedFrom=en " : This is not the standard way to initiate a translation request and seems to suggest a source language, which is unnecessary if auto-detection is desired/possible.
" /translate?to=en " : This is the correct and standard way to request a translation where the source language is automatically detected , and the target language is English ( en ) . This fulfills the translation requirement.
" /detect?to=en " / " /detect?from=en " : These endpoints are used for language detection and not for the translation itself. The prompt explicitly asks to develop code to translate the text.
文を正しく完成させる答えを選択してください。
Correct Answer:
Explanation:
Azure Synapse Analytics (formerly Azure SQL Data Warehouse) uses a massively parallel processing (MPP) architecture for big data and analytics workloads.
Control Node:
Acts as the brain of the system.
Receives queries, optimizes them, and then coordinates execution.
Does not perform large-scale data crunching itself.
Compute Nodes:
Perform the actual parallel execution of queries.
Data is distributed across these nodes.
The MPP engine breaks down queries into smaller tasks and distributes them to compute nodes for fast, parallel execution.
Review of Answer Options:
Distributes processing across compute nodes # Correct. This is the essence of MPP.
Distributes processing across control nodes # Incorrect. The control node does not process queries in parallel; it coordinates them.
Redirects client connections across compute nodes # Incorrect. Client connections go to the control node, not directly to compute nodes.
Redirects client connections across control nodes # Incorrect. There is typically one control node per Synapse instance.
The answer: Distributes processing across compute nodes
Microsoft References
Azure Synapse Analytics architecture
Distributed query processing in Azure Synapse
多言語チャットボットを構築しています。
ポジティブメッセージとネガティブメッセージに対して異なる回答を送信する必要があります。
どの2つのTextAnalyticsAPIを使用する必要がありますか？それぞれの正解は、解決策の一部を示しています。（2つ選択してください。）注：正しい選択はそれぞれ1ポイントの価値があります。
- A. 感情分析
- B. 言語を検出する
- C. 固有表現抽出
- D. 主要なフレーズ
- E. 有名なナレッジベースからのリンクされたエンティティ
Correct Answer: A,B 🗳️
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