[2026年07月24日] 合格させるMicrosoft AI-900試験情報と無料練習テスト
AI-900試験問題集PDF更新された問題集にはGoShiken試験合格保証付き
この試験は、AIやAzureの経験がほとんどない、またはまったくない個人に対応するために作成されましたが、概念の基本的な理解を開発することに興味があります。この試験は、ヘルスケア、財務、小売など、さまざまな業界でのAIの基本とそのアプリケーションの基本を完全に理解することに焦点を当てています。
AI-900試験では、機械学習、自然言語処理、コンピュータービジョン、会話AIなど、人工知能に関連するさまざまなトピックをカバーしています。また、AIの倫理的な考慮事項と責任ある使用に焦点を当てています。この試験は、データサイエンス、ソフトウェアエンジニアリング、またはクラウドコンピューティングのキャリアを追求することに興味がある個人に最適です。
Microsoft AI-900認定を取得することは、AIコンセプトの基本的な理解と、Azureプラットフォーム上でのその適用方法を示すものです。この認定は、データサイエンス、機械学習、およびAI開発などの分野で、個人が求職市場で目立つことを支援し、新しいキャリア機会を開拓することができます。また、Azure AIサービスを活用してイノベーションとビジネス成長を促進する熟練した専門家チームを構築することもできます。
質問 # 120
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 # 121
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?
- A. natural language processing
- B. computer vision
- C. conversational AI
- D. anomaly detection
正解：B
解説：
Explanation
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.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
質問 # 122
You need to develop a chatbot for a website. The chatbot must answer users questions based on the information m the following documents
* A product troubleshooting guide m a Microsoft Word document
* A frequently asked questions (FAQ) list on a webpage
Which service should you use to process the documents?
- A. Language Undemanding
- B. QnA Maker
- C. Text Analytics
- D. Azure Bot Service
正解：B
解説：
QnA Maker (now part of Azure AI Language Service - Custom Question Answering) enables bots to answer user queries by extracting questions and answers from structured sources like FAQs, webpages, and documents (e.g., Word, PDF). In this scenario, where data comes from a Microsoft Word troubleshooting guide and a FAQ webpage, QnA Maker automatically extracts knowledge and creates a searchable knowledge base.
Other options are incorrect:
* A. Language Understanding (LUIS): Detects intents, not question-answer pairs.
* B. Text Analytics: Extracts key phrases and sentiment but does not support conversational responses.
* C. Azure Bot Service: Hosts bots, but it requires QnA Maker for knowledge sources.
質問 # 123
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:
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overview
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/ You can use the Speech service to transcribe a call to text - Yes we can use Speech to Text API to achieve this
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction You can use a speech service to translate the audio of a call to a different language - Yes we can use Speech translation service to achieve this The Speech service includes the following application programming interfaces (APIs):
Speech-to-text - used to transcribe speech from an audio source to text format.
Text-to-speech - used to generate spoken audio from a text source.
Speech Translation - used to translate speech in one language to text or speech in another.
https://docs.microsoft.com/en-us/learn/modules/translate-text-with-translation-service/2-get-started-azure You can use text analytics service to extract key entities from a call transcript -Yes Text Analytics API helps to achieve this
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
質問 # 124
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
正解：
解説：
Explanation:
質問 # 125
You are building an Al-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible Al principle?
- A. fairness
- B. transparency
- C. accountability
- D. inclusiveness
正解：B
質問 # 126
You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
- A. Set Primary metric to accuracy.
- B. Set Max concurrent iterations to 0.
- C. Set Validation type to Auto.
- D. Enable Explain best model.
正解：D
解説：
Model Explain Ability.
Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/
質問 # 127
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
正解：
解説：
Explanation:
The correct matching aligns directly with the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under "Identify features of Azure Machine Learning". Azure Machine Learning provides a suite of tools that serve different functions within the model development lifecycle - from creating workspaces, to training models, to automating experimentation.
* The Azure portal # Create a Machine Learning workspace.The Azure portal is a web-based graphical interface for managing all Azure resources. According to Microsoft Learn, you use the portal to create and configure the Azure Machine Learning workspace, which acts as the central environment where datasets, experiments, models, and compute resources are organized. Creating a workspace through the portal involves specifying a subscription, resource group, and region - tasks that are part of the setup stage rather than model development.
* Machine Learning designer # Use a drag-and-drop interface used to train and deploy models.The Machine Learning designer (formerly "Azure ML Studio (classic)") provides a visual, no-code/low- code interface for building, training, and deploying machine learning pipelines. The designer uses a drag-and-drop workflow where users connect modules representing data transformations, model training, and evaluation. This tool is ideal for beginners and those who want to quickly experiment with machine learning concepts without writing code.
* Automated machine learning (Automated ML) # Use a wizard to select configurations for a machine learning run.Automated ML simplifies model creation by automatically selecting algorithms, hyperparameters, and data preprocessing options. Users interact through a guided wizard (within the Azure Machine Learning studio) that walks them through configuration steps such as selecting datasets, target columns, and performance metrics. The system then iteratively trains and evaluates multiple models to recommend the best-performing one.
Together, these tools streamline the machine learning workflow:
* Azure portal for setup and resource management,
* Machine Learning designer for visual model creation, and
* Automated ML for guided, automated model selection and tuning.
質問 # 128
Select the .
正解：
解説：
質問 # 129
Select the answer that correctly completes the sentence.
正解：
解説：
質問 # 130
You have a dataset that contains experimental data for fuel samples.
You need to predict the amount of energy that can be obtained from a sample based on its density.
Which type of Al workload should you use?
- A. Knowledge mining
- B. Classification
- C. Regression
- D. Clustering
正解：C
質問 # 131
Which format should you use to send requests to a REST API endpoint for Azure OpenAI?
- A. YAML
- B. CSV
- C. XML
- D. JSON
正解：D
解説：
When interacting with the Azure OpenAI REST API, requests and responses are structured using the JSON format. According to the Microsoft Learn documentation on the Azure OpenAI REST API, JSON is the required format for sending prompts, configuration parameters (such as temperature, max_tokens, or top_p), and metadata to the API endpoint.
A typical request to an Azure OpenAI endpoint (for example, /openai/deployments/{model-name}/chat
/completions?api-version=2024-02-01) includes a JSON body similar to the following:
{
"messages": [
{"role": "system", "content": "You are an assistant."},
{"role": "user", "content": "Write a JavaScript function to add two numbers."}
],
"max_tokens": 200,
"temperature": 0.7
}
This JSON format allows for structured data exchange between the client and the server, ensuring that key- value pairs are properly parsed by the API.
* Option A (CSV): Used for storing tabular data, not suitable for API communication.
* Option C (XML): Though historically used in web services, Microsoft's modern APIs, including Azure OpenAI, rely on JSON.
* Option D (YAML): Common in configuration files but not for REST API payloads.
Therefore, per official Microsoft documentation and the AI-900 study guide, the correct and verified answer is B. JSON, as it is the standard format required when sending requests to the Azure OpenAI REST API endpoint.
質問 # 132
You are processing photos of runners in a race.
You need to read the numbers on the runners' shirts to identity the runners in the photos.
Which type of computer vision should you use?
- A. object detection
- B. facial recognition
- C. optical character recognition (OCR)
- D. semantic segmentation
正解：C
解説：
Explanation
Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
質問 # 133
When training a model, why should you randomly split the rows into separate subsets?
- A. to train multiple models simultaneously to attain better performance
- B. to test the model by using data that was not used to train the model
- C. to train the model twice to attain better accuracy
正解：B
質問 # 134
You are developing a chatbot solution in Azure.
Which service should you use to determine a user's intent?
- A. Speech
- B. Language Understanding (LUIS)
- C. QnA Maker
- D. Translator Text
正解：B
解説：
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
質問 # 135
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation:
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
質問 # 136
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 # 137
......
あなたを合格させるMicrosoft試験にはAI-900試験問題集：https://www.goshiken.com/Microsoft/AI-900-mondaishu.html
AI-900試験問題集でMicrosoft練習テスト問題：https://drive.google.com/open?id=11MtIT_AyY7rU3THymsU2d69khsuRD2_V