Microsoft Azure AI Fundamentals (AI-900日本語版): AI-900日本語 Exam Premium File as PDF & Test Engine

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).

Correct Answer:

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.

Correct Answer:

Explanation:

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure

Correct Answer:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", there are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, two common approaches are regression and classification, while clustering is a primary example of unsupervised learning.
* "You train a regression model by using unlabeled data." - No.Regression models are trained with labeled data, meaning the input data includes both features (independent variables) and target labels (dependent variables) representing continuous numerical values. Examples include predicting house prices or sales forecasts. Unlabeled data (data without target output values) cannot be used to train regression models; such data is used in unsupervised learning tasks like clustering.
* "The classification technique is used to predict sequential numerical data over time." - No.
Classification is used for categorical predictions, where outputs belong to discrete classes, such as spam
/not spam or disease present/absent. Predicting sequential numerical data over time refers to time series forecasting, which is typically a regression or forecasting problem, not classification. The AI-900 syllabus clearly separates classification (categorical prediction) from regression (continuous value prediction) and time series (temporal pattern analysis).
* "Grouping items by their common characteristics is an example of clustering." - Yes.This statement is correct. Clustering is an unsupervised learning technique used to group similar data points based on their features. The AI-900 study materials describe clustering as the process of "discovering natural groupings in data without predefined labels." Common examples include customer segmentation or document grouping.
Therefore, based on Microsoft's AI-900 training objectives and definitions:
* Regression # supervised learning using labeled continuous data (No)
* Classification # categorical prediction, not sequential numeric forecasting (No)
* Clustering # grouping by similarity (Yes)