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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe automated machine learning |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Speech |
| Topic 4: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe use cases for generative AI |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer |
JPNTestにたくさんのIT専門人士がいって、弊社の問題集に社会のITエリートが認定されて、弊社の問題集は試験の大幅カーバして、合格率が１００％にまで達します。弊社のみたいなウエブサイトが多くても、彼たちは君の学習についてガイドやオンラインサービスを提供するかもしれないが、弊社はそちらにより勝ちます。JPNTestは同業の中でそんなに良い地位を取るの原因は弊社のかなり正確な試験の練習問題と解答そえに迅速の更新で、このようにとても良い成績がとられています。そして、弊社が提供した問題集を安心で使用して、試験を安心で受けて、君のMicrosoft AI-900認証試験の１００％の合格率を保証しますす。
質問 # 28
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
Text Description automatically generated
質問 # 29
You need to create a model that labels a collection of your personal digital photographs.
Which Azure Al service should you use?
正解：C
解説：
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of Computer Vision workloads on Azure", the Azure AI Custom Vision service allows users to build, train, and deploy custom image classification or object detection models. It is specifically designed for scenarios where you need a model tailored to your unique image dataset - in this case, personal digital photographs.
Custom Vision lets you upload and label your own images (for example, "family," "friends," "vacations," or
"pets") and then train a model that learns to recognize those categories. The system automatically extracts relevant features from the training images and creates a model capable of classifying new images into the predefined labels. You can iteratively refine your model by adding more images or re-training to improve accuracy.
The other options do not fit this requirement:
* A. Azure AI Language deals with text-based tasks such as language understanding, sentiment analysis, and key phrase extraction - not image processing.
* B. Azure AI Computer Vision provides prebuilt image analysis models (e.g., object detection, tag generation, scene description), but it cannot learn custom categories unique to your dataset. It's great for general image recognition but not for specialized labeling tasks.
* C. Azure AI Document Intelligence (Form Recognizer) is used to extract information from structured or semi-structured documents such as forms, invoices, or receipts - not photographs.
Therefore, when you need to label or categorize personal photos with custom-defined tags, the right service is Azure AI Custom Vision, because it allows you to build a model trained specifically on your own collection of images.
質問 # 30
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer.
What should you do?
正解：A
解説：
According to the Microsoft Learn module "Explore fundamental principles of machine learning" and the AI-
900 Official Study Guide, when building and evaluating a model (such as a clustering model) in Azure Machine Learning designer, data must be divided into two subsets:
* Training dataset: Used to train the model so it can learn patterns and relationships in the data.
* Testing dataset: Used to evaluate the model's performance on unseen data, ensuring that it generalizes well and does not overfit.
In Azure ML Designer, this is typically done using the Split Data module, which separates the dataset into training and testing portions (for example, 70% training and 30% testing). After training, you connect the testing dataset to an Evaluate Model module to assess metrics such as accuracy, precision, or silhouette score (for clustering).
Other options are incorrect:
* A. Split into features and labels: Clustering is an unsupervised learning technique, so it doesn't use labeled data.
* B. Use training dataset for evaluation: This would cause overfitting, as the model is being tested on the same data it learned from.
* D. Use the original dataset for training and evaluation: Also causes overfitting, offering no measure of generalization.
質問 # 31
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/bot-service/bot-builder-tutorial-add-qna
質問 # 32
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
質問 # 33
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JPNTestは MicrosoftのAI-900認定試験の認証に対して特別な教育ツールで、あなたに多くの時間とお金が使わないようにIT技術にも身につけさせるサイトでございます。JPNTestは専門家チームが自分の知識と経験をを利用してMicrosoftのAI-900「Microsoft Azure AI Fundamentals」認証試験の問題集を研究したものでございます。
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