最新的Microsoft Azure AI Fundamentals (AI-900 Korean Version) - AI-900 Korean免費考試真題

Explanation:
Features
The correct completion of the sentence is:
"In a machine learning model, the data that is used as inputs are called features." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types," the term feature refers to an individual measurable property or characteristic of the data that is used by a machine learning model to make predictions or decisions.
In supervised and unsupervised learning, features serve as the inputs to the model. They are the variables that represent the information the algorithm learns from. For example, if a model predicts the price of a house, the features might include square footage, number of bedrooms, location, and age of the property. These features feed into the model so that it can learn the relationship between inputs and outputs.
Microsoft Learn further defines these key concepts:
* Features: Input variables (independent variables) used by the model to learn patterns.
* Labels: The desired output or target variable that the model is trained to predict (e.g., price, category).
* Instances: Individual rows or data records within the dataset (each instance has multiple features).
* Functions: Algorithms or mathematical operations used during training and prediction - not data inputs.
Therefore, among the provided options - features, functions, labels, instances - only features accurately describe the data elements used as inputs for training or inference in a machine learning model.
In summary, within the AI-900 learning context:
* Features = inputs to the model.
* Labels = outputs for supervised learning.
* Instances = examples (rows) of data.

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Explanation:

According to Microsoft's Responsible AI principles, one of the key guiding values is Reliability and Safety, which ensures that AI systems operate consistently, accurately, and safely under all intended conditions. The AI-900 study materials and Microsoft Learn modules explain that an AI system must be trustworthy and dependable, meaning it should not produce results when the input data is incomplete, corrupted, or significantly outside the expected range.
In the given scenario, the AI system avoids providing predictions when important fields contain unusual or missing values. This behavior demonstrates reliability and safety because it prevents the system from making unreliable or potentially harmful decisions based on bad or insufficient data. Microsoft emphasizes that AI systems must undergo extensive validation, testing, and monitoring to ensure stable performance and predictable outcomes, even when data conditions vary.
The other options do not fit this scenario:
* Inclusiveness ensures that AI systems are accessible to and usable by all people, regardless of abilities or backgrounds.
* Privacy and Security focuses on protecting user data and ensuring it is used responsibly.
* Transparency involves making AI decisions explainable and understandable to humans.
Only Reliability and Safety directly address the concept of an AI system refusing to act or returning an error when it cannot make a trustworthy prediction. This principle helps prevent inaccurate or unsafe outputs, maintaining confidence in the system's integrity.
Therefore, ensuring an AI system does not produce predictions when input data is incomplete or unusual aligns directly with Microsoft's Reliability and Safety principle for responsible AI.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's modules on Computer Vision, object detection is the AI technique used to identify and locate multiple objects within an image. Unlike simple image classification, which only labels an entire image with a single category (for example, "This is a product"), object detection not only identifies the type of object but also pinpoints its exact position by providing bounding boxes and coordinates within the image.
In the scenario described - identifying the location of products on a conveyor belt - the system must be able to detect multiple items simultaneously and determine their spatial positions. Object detection algorithms (such as YOLO, Faster R-CNN, or SSD) are specifically designed for this purpose. This allows automation systems, like robotic arms or quality inspection systems, to track product locations in real time for sorting, packaging, or defect detection.
Let's evaluate the other options:
* Image classification only determines what is in the image, not where it is located. It cannot handle multiple objects or their positions.
* Image processing involves operations like resizing, filtering, or adjusting contrast, not understanding object placement.
* Optical character recognition (OCR) extracts text from images and documents, unrelated to locating physical items.
Thus, per Microsoft Learn's AI-900 guidance, object detection is the correct computer vision capability when a task requires both identification and spatial localization of items in an image or video stream.
# Final answer Object detection

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Explanation:
Clustering.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", clustering is an unsupervised machine learning technique used to group data points into distinct segments or clusters based on shared characteristics.
Unlike supervised learning (classification or regression), clustering works with unlabeled data, discovering natural groupings without predefined outcomes.
In this question, Recency, Frequency, and Monetary (RFM) values are common marketing metrics used to evaluate customer behavior:
* Recency - how recently a customer made a purchase.
* Frequency - how often they make purchases.
* Monetary - how much money they spend.
Using RFM analysis, a company can segment its customers into groups such as "loyal," "occasional," or "at- risk" buyers. This segmentation process does not rely on predefined labels but rather discovers patterns within the data - which is the defining characteristic of clustering.
In the AI-900 context, clustering is described as a method that "groups items with similar features so that items in the same group are more similar to each other than to those in other groups." Common algorithms used include K-Means, Hierarchical Clustering, and DBSCAN, all available within Azure Machine Learning Designer and other Azure ML environments.
To clarify the incorrect options:
* Classification is supervised learning used to predict discrete categories (e.g., yes/no, spam/not spam).
* Regression predicts continuous numeric values (e.g., house prices).
* Regularization is a model optimization technique, not a type of machine learning.
Therefore, when businesses use RFM values to identify customer segments without labeled outcomes, this is an application of unsupervised learning through clustering.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) study guide, the three main types of supervised and unsupervised machine learning models-classification, clustering, and regression-are used for distinct problem types depending on the structure of the data and the prediction goal.
* Clustering is an unsupervised learning technique used when the goal is to group items with similar characteristics without predefined labels. In this scenario, "Assign categories to passengers based on demographic data" implies automatically grouping passengers based on patterns such as age, income, or travel frequency, without any prior labeling. This directly maps to clustering, which discovers hidden groupings (for example, segmenting customers into categories like business travelers or vacationers).
* Regression is a supervised learning method used to predict continuous numerical values. The scenario
"Predict the amount of consumed fuel based on flight distance" is a classic regression problem because the output (fuel consumption) is a continuous variable dependent on another continuous variable (distance). Regression models, such as linear regression, are trained to estimate numeric outputs.
* Classification is also a supervised learning approach, but it predicts discrete categories or outcomes.
The scenario "Predict whether a passenger will miss their flight based on demographic data" involves a binary decision (missed or not missed), which is typical of classification tasks. These models learn from labeled examples to assign new instances to specific categories.
In summary, Clustering groups similar passengers, Regression predicts continuous numerical outcomes, and Classification determines categorical outcomes. This alignment precisely matches the definitions in Microsoft' s AI-900 learning objectives under "Describe common machine learning types and scenarios."

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Explanation:

The correct answer is "adding and connecting modules on a visual canvas." According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore automated machine learning in Azure Machine Learning," the Azure Machine Learning designer is a drag-and-drop, no-code environment that allows users to create, train, and deploy machine learning models visually. It is specifically designed for users who prefer an intuitive graphical interface rather than writing extensive code.
Microsoft Learn defines Azure Machine Learning designer as a tool that allows you to "build, test, and deploy machine learning models by dragging and connecting pre-built modules on a visual canvas." These modules can represent data inputs, transformations, training algorithms, and evaluation processes. By linking them together, users can create an end-to-end machine learning pipeline.
The designer simplifies the machine learning workflow by allowing data scientists, analysts, and even non- developers to:
* Import and prepare datasets visually.
* Choose and connect algorithm modules (e.g., classification, regression, clustering).
* Train and evaluate models interactively.
* Publish inference pipelines as web services for prediction.
Let's analyze the other options:
* Automatically performing common data preparation tasks - This describes Automated ML (AutoML), not the Designer.
* Automatically selecting an algorithm to build the most accurate model - Also a characteristic of AutoML, where the system tests multiple algorithms automatically.
* Using a code-first notebook experience - This describes the Azure Machine Learning notebooks environment, which uses Python and SDKs, not the Designer interface.
Therefore, based on the official AI-900 learning objectives and Microsoft Learn documentation, the Azure Machine Learning designer allows you to create models by adding and connecting modules on a visual canvas, providing a no-code, interactive experience ideal for users building custom machine learning workflows visually.

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Explanation:
validation.
In the Microsoft Azure AI Fundamentals (AI-900) study materials, a key concept in machine learning model development is splitting data into subsets for training, validation, and testing. A randomly extracted subset of data from a dataset is most commonly used for validation - that is, for evaluating the performance of the model during or after training.
Here's how this process works:
* Training set - This portion of the dataset is used to train the machine learning model. The model learns patterns, relationships, and parameters from this data.
* Validation set - This is a randomly selected subset (separate from training data) used to fine-tune model hyperparameters and evaluate how well the model generalizes to unseen data. It helps detect overfitting
- when the model performs well on training data but poorly on new data.
* Test set - A final, untouched dataset used to measure the model's real-world performance after all training and tuning are complete.
By reserving a random subset for validation, data scientists ensure that the model's performance metrics reflect generalization, not memorization of the training data.
Let's review the incorrect options:
* Algorithms - These are the mathematical frameworks or methods used to build models (e.g., decision trees, neural networks). They are not data subsets.
* Features - These are input variables (attributes) used by the model, not randomly selected data subsets.
* Labels - These are target values or outcomes the model predicts; again, not data subsets.
Therefore, in alignment with Azure AI-900's machine learning fundamentals, the correct completion is:
# "A randomly extracted subset of data from a dataset is commonly used for validation of the model."