Free Microsoft AI-900日本語 Practice Test & Real Exam Questions

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Explanation:

In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision workloads are grouped into distinct types, each serving a specific purpose. The three major workloads illustrated here are image classification, object detection, and optical character recognition (OCR). Understanding their use cases is essential for correctly mapping them to real-world scenarios.
* Generate captions for images # Image classificationThe image classification workload is used to identify the main subject or context of an image and assign descriptive labels. In Microsoft Learn's
"Describe features of computer vision workloads," image classification models are trained to recognize content (e.g., a cat, a beach, or a city). Caption generation expands on classification results by describing the image's contents in human-readable language-based on what the model identifies as key visual features.
* Extract movie title names from movie poster images # Optical character recognition (OCR)OCR is a vision workload that detects and extracts text from images. Azure AI Vision's Read API or Document Intelligence OCR models can identify printed or handwritten text within posters, signs, or documents.
In this case, the movie title text from a poster is best extracted using OCR.
* Locate vehicles in images # Object detectionThe object detection workload identifies multiple objects within an image and provides their locations using bounding boxes. It's ideal for tasks like counting cars in a parking lot or tracking objects in traffic images.

Explanation:

The Azure OpenAI DALL-E model is a generative image model designed to create original images from textual descriptions (prompts). According to the Microsoft Learn documentation and the AI-900 study guide, DALL-E's primary function is text-to-image generation-it converts creative or descriptive text input into visually relevant imagery.
* "Generate captions for uploaded images" # NoDALL-E cannot create image captions. Captioning an image (describing what's in an uploaded image) is a vision analysis task, not an image generation task.
That functionality belongs to Azure AI Vision, which can analyze and describe images, detect objects, and generate captions automatically.
* "Reliably generate technically accurate diagrams" # NoWhile DALL-E can create visually appealing artwork or conceptual sketches, it is not designed for producing precise or technically correct diagrams, such as engineering schematics or architectural blueprints. The model's generative process emphasizes creativity and visual diversity rather than factual or geometric accuracy. Thus, it cannot be relied upon for professional technical outputs.
* "Generate decorative images to enhance learning materials" # YesThis is one of DALL-E's strongest use cases. It can generate decorative, conceptual, or illustrative images to enhance presentations, educational materials, and marketing content. It enables educators and designers to quickly produce unique visuals aligned with specific themes or topics, enhancing engagement and creativity.

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Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe core concepts of machine learning on Azure", the Azure Machine Learning Designer is a drag-and-drop, no-code/low-code interface that allows users to build, test, and deploy machine learning models visually without needing to write extensive code.
* Drag-and-Drop Visual Canvas # YESThe Azure Machine Learning Designer indeed provides a graphical interface where users can connect prebuilt modules for data preprocessing, training, evaluation, and deployment. Microsoft documentation describes it as a "drag-and-drop visual environment that simplifies machine learning model creation." This allows beginners and business users to construct machine learning pipelines intuitively, confirming this statement as True.
* Save Progress as a Pipeline Draft # YESThe designer lets users save their current work as a pipeline draft, enabling them to pause and return later. Microsoft Learn explicitly states that you can "save and publish pipeline drafts before running or deploying them." This functionality ensures workflow continuity, collaboration, and version management-making this statement also True.
* Include Custom JavaScript Functions # NOThe Azure Machine Learning Designer allows the integration of Python scripts through the "Execute Python Script" module for custom logic, but it does not support JavaScript. Custom code in the designer environment is limited to Python, as the platform is built for data science and machine learning tasks typically handled in Python-based environments.
Therefore, this statement is False.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore fundamental principles of machine learning", feature engineering is the process used to generate additional features or transform existing data into forms that improve model performance. Features are individual measurable properties or characteristics used as input for machine learning algorithms. The goal of feature engineering is to create new informative variables that better represent the underlying patterns in the data.
Feature engineering may include tasks such as:
* Combining or transforming raw data columns (e.g., creating a "total purchase amount" from price × quantity).
* Extracting time-based components (e.g., year, month, day, hour) from datetime values.
* Encoding categorical variables (e.g., one-hot encoding or label encoding).
* Scaling or normalizing numerical features.
* Creating polynomial or interaction terms to capture complex relationships.
Microsoft's AI-900 learning material emphasizes that the process of preparing data for machine learning involves data cleaning, feature engineering, and feature selection. While feature selection is about choosing the most relevant features from the existing dataset, feature engineering focuses on creating or generating new features to enhance model accuracy and generalization.
The other options do not fit this definition:
* Feature selection is about removing redundant or irrelevant features, not generating new ones.
* Model evaluation involves assessing the model's performance using metrics like accuracy or F1 score.
* Model training is the phase where the algorithm learns patterns from the data, not when features are created.
Therefore, based on the AI-900 official concepts and Microsoft's documentation, the correct answer is Feature engineering, as it is the process specifically used to generate additional features that improve machine learning model performance and predictive capability.

Explanation:

In machine learning, especially within the Microsoft Azure AI Fundamentals (AI-900) framework, datasets used for supervised learning are composed of features (inputs) and labels (outputs). According to the Microsoft Learn module "Explore the machine learning process", a feature is any measurable property or attribute used by the model to make predictions, whereas a label is the actual value or category the model is trying to predict.
* Household Income # FeatureA feature (also known as an independent variable) represents the input data that the machine learning algorithm uses to detect patterns or correlations. In this dataset, Household Income is a numeric value that influences the prediction of house price categories. During training, the model learns how variations in household income correlate with changes in the house price category.
Microsoft Learn defines features as "the attributes or measurable inputs that are used to train the model." Thus, Household Income serves as a predictive input or feature.
* House Price Category # LabelThe label (or dependent variable) represents the output the model aims to predict. It is the known result during training that helps the algorithm learn correct mappings between features and outcomes. In this scenario, House Price Category-which can take values such as "Low,"
"Middle," or "High"-is the classification outcome that the model will predict based on household income (and possibly other variables). According to Microsoft Learn, "the label is the variable that contains the known values that the model is trained to predict." In summary, the dataset defines a supervised learning classification problem, where Household Income is the feature (input) and House Price Category is the label (output) that the model will learn to predict.

Explanation:

"When evaluating the performance of a model, the confusion matrix displays the predicted and actual positives and negatives by using a grid of 0 and 1 values." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types", a confusion matrix is a tool used to evaluate the performance of classification models. It visually summarizes how many predictions were correctly or incorrectly classified by comparing the predicted labels to the actual (true) labels.
A confusion matrix is a table, typically 2×2 for binary classification, with the following components:
* True Positives (TP): The model correctly predicted the positive class.
* True Negatives (TN): The model correctly predicted the negative class.
* False Positives (FP): The model incorrectly predicted the positive class.
* False Negatives (FN): The model incorrectly predicted the negative class.
The confusion matrix allows data scientists and analysts to derive important performance metrics such as accuracy, precision, recall, and F1-score, which together provide a more complete understanding of how well a model performs beyond a single number.
In Microsoft Learn's AI-900 curriculum, the confusion matrix is highlighted as a key visualization tool that
"compares actual values to predicted values to evaluate classification performance." The grid format (using 0s and 1s for predicted classes) helps identify where misclassifications occur.
By contrast:
* AUC metric (Area Under Curve) and ROC curve evaluate model discrimination ability.
* Threshold defines decision cutoffs but doesn't display classifications.
Therefore, based on the official Microsoft AI-900 study guide and Microsoft Learn resources, the correct answer is Confusion Matrix, as it provides a grid view comparing actual versus predicted values in classification models.

Explanation:
Azure Bot Service and Azure Cognitive Services can be integrated. # Yes
* Azure Bot Service engages with customers in a conversational manner. # Yes
* Azure Bot Service can import frequently asked questions (FAQ) to question and answer sets. # Yes
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All three statements are true, as confirmed by the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore conversational AI." The Azure Bot Service is Microsoft's platform for building, deploying, and managing intelligent bots that can communicate naturally with users across various channels (web, Teams, Facebook Messenger, etc.).
* Azure Bot Service and Azure Cognitive Services can be integrated # YesMicrosoft Learn specifies that Azure Bot Service can be enhanced with Azure Cognitive Services such as Language Understanding (LUIS), QnA Maker, and Speech Services to add intelligence. For example, integration with LUIS allows bots to understand user intent and context, while QnA Maker helps them respond accurately to FAQs. As stated in the official documentation: "The Azure Bot Service can be combined with Cognitive Services to create bots that understand language, speech, and meaning."
* Azure Bot Service engages with customers in a conversational manner # YesThe primary function of Azure Bot Service is to create conversational AI agents that interact naturally with users. These bots simulate human-like dialogue using text or speech. According to Microsoft Learn, "Bots created using Azure Bot Service communicate with users in a conversational format through natural language."
* Azure Bot Service can import frequently asked questions (FAQ) to question and answer sets # YesAzure Bot Service can integrate with the QnA Maker (now part of Azure Cognitive Service for Language) to automatically import FAQs from existing documents or web pages and generate a knowledge base of question-answer pairs. This allows the bot to respond intelligently to customer queries.
In conclusion, Azure Bot Service supports intelligent, conversational interaction, integrates seamlessly with Cognitive Services, and can use QnA Maker to import and manage FAQ-based knowledge sets-making all three statements true.

Explanation:

The correct answer is Azure AI Language, which includes the Question Answering capability (previously known as QnA Maker). According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, the Azure AI Language service can be used to create a knowledge base from frequently asked questions (FAQ) and other structured or semi-structured text sources.
This service allows developers to build intelligent applications that can understand and respond to user questions in natural language by referencing prebuilt or custom knowledge bases. The Question Answering feature extracts pairs of questions and answers from documents, websites, or manually entered data and uses them to construct a searchable knowledge base. This knowledge base can then be integrated with Azure Bot Service or other conversational platforms to create interactive, self-service chatbots.
Here's how it works:
* Developers upload FAQ documents, URLs, or structured content.
* Azure AI Language processes the content and identifies logical question-answer pairs.
* The model stores these pairs in a knowledge base that can be queried by user input.
* When users ask questions, the model finds the best matching answer using natural language understanding techniques.
In contrast:
* Azure AI Document Intelligence (Form Recognizer) is used to extract structured data from forms and documents, not to create FAQ knowledge bases.
* Azure AI Bot Service is for managing and deploying conversational bots but does not generate knowledge bases.
* Microsoft Bot Framework SDK provides tools for building conversational logic but still requires a knowledge source like Question Answering from Azure AI Language.
Therefore, the service that can create a knowledge base from FAQ content is Azure AI Language.

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