2026年ShikenPASSの最新AI-900 PDFダンプおよびAI-900試験エンジンの無料共有：https://drive.google.com/open?id=1iCkvavv53_e_ASzSzVfWrfFMDiWoGyW2
安全かつ最も信頼性の高いMicrosoft AI-900問題集販売サイトとして、我々はお客様の個人情報を内緒し、支払いの安全性を保証しています。だから、我々社のMicrosoft AI-900問題集のさまざまなバージョンを安心に購買できます。弊社は量豊かのIT試験資料を所有するから、あなたは別のMicrosoft AI-900試験に関心を寄せるなら、ShikenPASSでは需要したい資料を尋ねたり、弊社の職員に問い合わせたりしています。
| Section | Weight | Objectives |
|---|---|---|
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Identify common machine learning tasks - Describe features of no-code automated ML - Describe core machine learning concepts |
| Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities |
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Describe Azure capabilities for NLP - Identify Azure AI services for NLP |
試験の受験者向けの多数のAI-900学習質問があることは認められていますが、非常に多くの資料のすべての重要なポイントを自分で要約することは不可能です。しかし、あなたはAI-900練習資料のこのウェブサイトをクリックしたので、この問題を解決するために当社が特にここにいるので、それについて全く心配する必要はありません。 AI-900の実際の試験がどれほど有用で効果的であるかを理解しているため、長期的な協力を求める多くの常連客がいます。トレーニング資料の輝点について一般的な考えをお伝えできるように、トレーニングの利点を3つ挙げます。
質問 # 116
You use drones to identify where weeds grow between rows of crops to send an Instruction for the removal of the weeds. This is an example of which type of computer vision?
正解：B
解説：
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.
Reference:
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overview
質問 # 117
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point
正解：
解説：
質問 # 118
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
正解：C
解説：
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
質問 # 119
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation:
In the context of Microsoft Azure AI Fundamentals (AI-900) and general machine learning principles, regression refers to a type of supervised learning used to predict continuous numerical values based on historical data. The goal of regression is to model the relationship between input variables (features) and a continuous output variable (target).
In this scenario, the task is to predict how many vehicles will travel across a bridge on a given day. The number of vehicles is a numerical value that can vary continuously depending on factors such as time of day, weather, weekday/weekend, or traffic trends. Because the output is numeric and not categorical, this problem type clearly fits into regression analysis.
Microsoft's official learning content for AI-900, under "Identify features of regression and classification machine learning models," specifies that regression models are used to predict values such as sales forecasts, demand estimation, temperature prediction, or traffic volume-all of which share the same underlying objective: predicting a quantity.
To clarify other options:
* Classification is used when predicting categories or discrete classes, such as determining whether an email is spam or not spam, or if an image contains a cat or a dog.
* Clustering is an unsupervised learning technique used to group similar data points without predefined labels (for example, grouping customers by purchasing behavior).
Since predicting the number of vehicles results in a continuous numerical output, it aligns precisely with the regression workload type described in the Microsoft AI-900 study materials.
質問 # 120
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation:
Features
質問 # 121
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ShikenPASSは2008年に設立されましたが、現在、ハイパスAI-900ガイドトレントマテリアルの評判が高いため、この分野で主導的な地位にあります。 AI-900試験問題には、長年にわたって多くの同級生が続いていますが、これを超えることはありません。過去10年以来、成熟した完全なAI-900学習ガイドR＆Dシステム、顧客の情報安全システム、顧客サービスシステムを構築しています。有効なAI-900準備資料を購入したすべての候補者は、高品質のガイドトレント、情報の安全性、および最高のカスタマーサービスを利用できます。
AI-900模擬問題集: https://www.shikenpass.com/AI-900-shiken.html
2026年ShikenPASSの最新AI-900 PDFダンプおよびAI-900試験エンジンの無料共有：https://drive.google.com/open?id=1iCkvavv53_e_ASzSzVfWrfFMDiWoGyW2