さらに、Japancert AI-900ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=10ur-HnVGlwNgX1VPWo07b5ZKaqWZR2nJ
AI-900学習教材のシステムはスムーズで、インストールすることも簡単です。だから、あなたの多くの貴重な時間を節約できます。インストールした後、AI-900学習教材を勉強できます。勉強するとき、問題の答えをちゃんと覚えると、AI-900試験に参加できます。AI-900学習教材の的中率が高いですので、多くの受験者は試験に合格しました。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Identify common machine learning tasks |
| Topic 3: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Topic 4: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Identify common NLP tasks - Describe Azure capabilities for NLP |
完全版を購入する前に、AI-900練習問題ダウンロードの無料PDFデモを提供しています。購入後、AI-900学習教材で1年間の無料アップデートと1年間のカスタマーサービスを提供します。また、AI-900トレーニングブレインダンプで「パス保証」をお約束します。私たちの目的は、合格率を最高100％にすることであり、顧客満足度の比率も100％です。有効なAI-900準備資料をお探しの場合は、お気軽に私たちを選んでください。
質問 # 49
You plan to use Azure Cognitive Services to develop a voice controlled personal assistant app.
Match the Azure Cognitive Services to the appropriate tasks.
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 selection is worth one point.
正解：
解説：
質問 # 50
Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle 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 # 51
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：
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
質問 # 52
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction
質問 # 53
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
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
質問 # 54
......
ほとんどの人は時間を節約するために速達を使用する傾向があるため、AI-900準備試験は購入後5〜10分以内に送信されます。プラットフォームで料金を支払う限り、指定された時間内に関連する試験資料をメールボックスに配信します。当社はサービス全体を非常に重視しており、AI-900試験資料の配信に問題がある場合：Microsoft Azure AI Fundamentals、お知らせください。メッセージまたは電子メールを利用できます。
AI-900模擬対策: https://www.japancert.com/AI-900.html
P.S.JapancertがGoogle Driveで共有している無料の2026 Microsoft AI-900ダンプ：https://drive.google.com/open?id=10ur-HnVGlwNgX1VPWo07b5ZKaqWZR2nJ