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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning |
| Topic 2: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Speech |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
Microsoftお客様にさまざまな種類のAI-900練習用トレントを提供して学習させ、知識の蓄積と能力の向上を支援したいと考えています。また、AI-900学習ガイドを使用して、すべてのユーザーの質問に最短時間で専門家が回答できることを保証します。もう1つ、散発的な時間を最大限に活用して知識と情報を吸収するお手伝いをします。つまり、AI-900試験対策を目指している他の類似企業と比較して、AI-900試験問題のサービスと品質は、お客様と潜在的なクライアントから高く評価されています。
質問 # 15
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation:
Text Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data
質問 # 16
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
Box 1: Yes
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Box 3: Yes
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
質問 # 17
Select the answer that correctly completes the sentence.
正解：
解説：
質問 # 18
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0 All 3 are correct as they are the different channels to connect with a bot Office 365 email - Enable a bot to communicate with users via Office 365 email.
Microsoft Teams - Configure a bot to communicate with users through Microsoft Teams.
Web Chat - Automatically configured for you when you create a bot with the Bot Framework Service.
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0
質問 # 19
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 20
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