Behind every Microsoft Azure AI Fundamentals (AI-900日本語版) product at GetValidTest stands a research team with one action plan: get you certified. The 336 practice questions they maintain turn that plan into your result.
Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities - Describe core machine learning concepts |
| Topic 2: Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision |
| Topic 3: 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 |
| Topic 4: Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities - Describe generative AI concepts |
| Topic 5: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
The Microsoft AI-900日本語 Exam, Explained
The Microsoft Azure AI Fundamentals (AI-900日本語版) syllabus is organized into 5 domains — including Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%), Describe features of computer vision workloads on Azure (15-20%), Describe features of Generative AI workloads on Azure (15-20%). The percentages show where the exam concentrates its points; weight your revision the same way. The complete outline is listed above.
45-60 for 40-60 questions — that's the official shape of the AI-900日本語 exam. Divide your minutes per question before test day, flag and skip anything that stalls you, and rehearse full timed runs in the GetValidTest engine until pacing is automatic.
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The Microsoft Azure AI Fundamentals (AI-900日本語版) is Microsoft's certification exam for Microsoft Certified: Azure AI Fundamentals, a Foundational-level credential. Employers value it because it verifies real skills, not attendance. Related credentials include Microsoft Certified: Azure AI Fundamentals.
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Microsoft Azure AI Fundamentals (AI-900日本語版) Sample Questions:
文を正しく完成させる答えを選択してください。
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", regression is a supervised machine learning technique used to predict continuous numerical values based on one or more input features. In this scenario, the task is to predict a vehicle's miles per gallon (MPG)-a continuous numeric value-based on several measurable factors such as weight, engine power, and other specifications.
Regression models learn the mathematical relationship between input variables (independent features) and a numeric target variable (dependent outcome). Common regression algorithms include linear regression, decision tree regression, and support vector regression. In the example, the model would analyze historical data of vehicles and learn patterns that map characteristics (like engine size, horsepower, and weight) to fuel efficiency. Once trained, it can predict the MPG for a new vehicle configuration.
The other options describe different problem types:
* Classification predicts discrete categories (for example, whether a car is "fuel efficient" or "not fuel efficient"), not continuous values.
* Clustering is an unsupervised learning method that groups data points based on similarities without predefined labels, not predictive modeling.
* Anomaly detection identifies data points that significantly deviate from normal patterns, such as detecting engine sensor failures or fraudulent transactions.
Since predicting MPG involves estimating a numeric value within a continuous range, regression is the most appropriate model type.
In summary, per AI-900 training content, regression models are used when the output variable is numeric, classification for categorical outputs, and clustering for pattern discovery. Therefore, predicting miles per gallon based on vehicle features is a textbook example of a regression problem in Azure Machine Learning.
自然言語処理（NLP）を使用して、ユーザーのテキスト入力に基づいて次のアクションを実行するチャットボットを構築しています。
*顧客の注文を受け入れます。
*サポートドキュメントを取得します。
*注文ステータスの更新を取得します。
どのタイプのNLPを使用する必要がありますか？
- A. 固有表現抽出
- B. 言語モデリング
- C. 感情分析
- D. 翻訳
Correct Answer: B 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
製品の品質を予測するAzureMachineLearningモデルがあります。モデルには、50,000レコードを含むトレーニングデータセットがあります。データのサンプルを次の表に示します。
次の各ステートメントについて、ステートメントが真の場合は[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Correct Answer:
Explanation:
This question tests the understanding of features and labels in machine learning, a core concept covered in the Microsoft Azure AI Fundamentals (AI-900) syllabus under "Describe fundamental principles of machine learning on Azure." In supervised machine learning, data is divided into features (inputs) and labels (outputs).
* Features are the independent variables - measurable properties or characteristics used by the model to make predictions.
* Labels are the dependent variables - the target outcome the model is trained to predict.
From the provided dataset, the goal of the Azure Machine Learning model is to predict product quality (Pass or Fail). Therefore:
* Mass (kg) is a feature - Yes"Mass (kg)" represents an input variable used by the model to learn patterns that influence product quality. It helps the algorithm understand how variations in mass might correlate with passing or failing the quality test. Thus, it is correctly classified as a feature.
* Quality Test is a label - YesThe "Quality Test" column indicates the outcome of the manufacturing process, marked as either Pass or Fail. This is the target the model tries to predict during training. In Azure ML terminology, this column is the label, as it represents the dependent variable.
* Temperature (C) is a label - No"Temperature (C)" is an input that helps the model determine quality outcomes, not the outcome itself. It influences the quality result but is not the value being predicted.
Therefore, temperature is another feature, not a label.
In conclusion, per Microsoft Learn and AI-900 study materials, features are measurable inputs (like mass and temperature), while the label is the target output (like the quality test result).
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Correct Answer:
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service and has nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna-bot LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
Webチャットボットの例はどのシナリオですか？
- A. コンサートのウェブサイトに入力されたレビューが肯定的か否定的かを判断し、レビューに賛成または反対の絵文字を追加します。
- B. キオスクで顧客が入力した英語の質問に翻訳して、適切な担当者が顧客に折り返し電話できるようにします。
- C. 電子メールで質問を受け入れ、メッセージの内容に基づいて電子メールメッセージを正しい人にルーティングします。
- D. ウェブサイトのインターフェースから、音楽祭の予定されているイベントやチケット購入に関する一般的な質問に答えます。
Correct Answer: D 🗳️
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