AI-900試験の準備方法｜実用的なAI-900認定内容試験｜素晴らしいMicrosoft Azure AI Fundamentals合格体験記

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CertJuken のMicrosoftのAI-900問題集はシラバスに従って、それにAI-900認定試験の実際に従って、あなたがもっとも短い時間で最高かつ最新の情報をもらえるように、弊社はトレーニング資料を常にアップグレードしています。弊社のAI-900のトレーニング資料を買ったら、一年間の無料更新サービスを差し上げます。もっと長い時間をもらって試験を準備したいのなら、あなたがいつでもサブスクリプションの期間を伸びることができます。

Microsoft AI-900 認定は、AI 分野に興味を持ち、この分野でキャリアを構築したい人にとって貴重な資格です。AI 技術と Azure サービスの堅固な基盤を提供し、革新的なソリューションの開発やビジネス成長の推進に活用することができます。

この試験では、AIワークロードや考慮事項、機械学習の基本的な概念、データサイエンスプロセス、Azure AIサービスなど、さまざまなトピックをカバーしています。候補者は、Azure AIサービスを特定し、その機能と利点を理解し、ビジネスの問題を解決するためにそれらを使用する方法を知る能力についてテストされます。この試験には、プライバシー、透明性、説明責任など、AIおよびMLに関連する倫理的および法的考慮事項に関する質問も含まれています。

Microsoft AI-900: Microsoft Azure AI Fundamentals Examは、人工知能（AI）の基本的なコンセプトとマイクロソフトAzureクラウドプラットフォームでのその適用に関する知識をテストするために設計された認定試験です。この試験は、AIおよび機械学習に関する知識とスキルを習得し、リアルワールドのビジネス課題を解決する方法に興味を持つ個人を対象としています。

>> AI-900認定内容 <<

AI-900合格体験記、AI-900試験対応

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Microsoft Azure AI Fundamentals 認定 AI-900 試験問題 (Q59-Q64):

質問 # 59
Select the answer that correctly completes the sentence.

正解：

解説：


質問 # 60
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation:

The correct answer is object detection. According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Explore computer vision", object detection is the process of identifying and locating objects within an image or video. The primary characteristic of object detection, as emphasized in the study guide, is its ability to return a bounding box around each detected object along with a corresponding label or class.
In this question, the task involves returning a bounding box that indicates the location of a vehicle in an image. This is the exact definition of object detection - identifying that the object exists (a vehicle) and determining its position within the frame. Microsoft Learn clearly differentiates this from other computer vision tasks. Image classification, for example, only determines what an image contains as a whole (for instance, "this image contains a vehicle"), but it does not indicate where in the image the object is located.
Optical character recognition (OCR) is specifically used for extracting printed or handwritten text from images, and semantic segmentation involves classifying every pixel in an image to understand boundaries in greater detail, often used in autonomous driving or medical imaging.
The official AI-900 guide highlights object detection as one of the key computer vision workloads supported by Azure Computer Vision, Custom Vision, and Azure Cognitive Services. These services are designed to detect multiple instances of various object types in a single image, outputting bounding boxes and confidence scores for each.
Therefore, based on the AI-900 official curriculum and Microsoft Learn concepts, returning a bounding box that shows the location of a vehicle is a textbook example of object detection, as it involves both recognition and localization of the object within the image frame.


質問 # 61
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?

正解：D

解説：
Section: Describe fundamental principles of machine learning on Azure
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud.
Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/


質問 # 62
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 # 63
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:

Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing


質問 # 64
......

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