Microsoft Azure AI Fundamentals (AI-900 Korean Version) - AI-900 Korean Exam Practice Test

Explanation:

In the context of Microsoft Azure AI Fundamentals (AI-900) and general machine learning principles, regression refers to a type of supervised learning used to predict continuous numerical values based on historical data. The goal of regression is to model the relationship between input variables (features) and a continuous output variable (target).
In this scenario, the task is to predict how many vehicles will travel across a bridge on a given day. The number of vehicles is a numerical value that can vary continuously depending on factors such as time of day, weather, weekday/weekend, or traffic trends. Because the output is numeric and not categorical, this problem type clearly fits into regression analysis.
Microsoft's official learning content for AI-900, under "Identify features of regression and classification machine learning models," specifies that regression models are used to predict values such as sales forecasts, demand estimation, temperature prediction, or traffic volume-all of which share the same underlying objective: predicting a quantity.
To clarify other options:
* Classification is used when predicting categories or discrete classes, such as determining whether an email is spam or not spam, or if an image contains a cat or a dog.
* Clustering is an unsupervised learning technique used to group similar data points without predefined labels (for example, grouping customers by purchasing behavior).
Since predicting the number of vehicles results in a continuous numerical output, it aligns precisely with the regression workload type described in the Microsoft AI-900 study materials.

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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.

Explanation:

App1: "Understands the public perception of a brand or topic" # Sentiment analysis According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn's Natural Language Processing (NLP) documentation, Sentiment analysis is a feature of the Azure AI Language Service that determines the emotional tone or attitude expressed in text. It classifies text as positive, negative, neutral, or mixed, which makes it ideal for analyzing customer opinions, brand perception, or product feedback.
For example, an organization can use sentiment analysis to process customer reviews or social media posts to determine how people feel about a particular brand or topic. This insight helps companies assess customer satisfaction, public perception, and marketing impact.
App2: "Applies profanity filters to speech-to-text" # Language detection The task of applying profanity filters occurs during or after speech-to-text transcription, which involves identifying the language used so that the correct filter can be applied. Language detection is an NLP feature that determines which language is being spoken or written. Once the language is detected, appropriate profanity filtering rules are automatically applied to remove or mask offensive words from transcribed text.
Other options such as Captioning or Named Entity Recognition (NER) are not relevant:
* Captioning describes images or videos, not speech filtering.
* NER identifies people, locations, or organizations but does not handle profanity or language detection.
Therefore, based on Azure AI NLP features:
* App1 uses Sentiment analysis
* App2 uses Language detection

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.

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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:
Yes, Yes, and No.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under the topic "Describe features of common AI workloads", conversational AI solutions like chatbots are used to automate and enhance customer interactions. A chatbot is an AI service capable of understanding user inputs (text or voice) and providing appropriate responses, often integrated into websites, mobile apps, or messaging platforms.
* A restaurant can use a chatbot to empower customers to make reservations using a website or an app - Yes.This statement is true because conversational AI is designed to handle structured tasks such as booking, scheduling, and information retrieval. Chatbots built with Azure Bot Service can connect to backend systems (like a reservation database) to let customers make or modify reservations through a chat interface. The AI-900 study guide explicitly notes that chatbots can help businesses "automate processes such as booking or reservations" to improve efficiency and customer experience.
* A restaurant can use a chatbot to answer inquiries about business hours from a webpage - Yes.This is also true. Chatbots can be trained using QnA Maker (now integrated into Azure AI Language) or Azure Cognitive Services for Language to answer common customer questions. FAQs such as opening hours, menu details, and directions are ideal for chatbot automation, as outlined in the AI-900 modules discussing customer support automation.
* A restaurant can use a chatbot to automate responses to customer reviews on an external website - No.
This is not a typical chatbot use case taught in AI-900. Chatbots are meant for direct interactions within controlled channels, such as a company's own website or messaging app. Managing and posting responses to reviews on external platforms (like Yelp or Google Reviews) would involve policy restrictions, authentication issues, and reputational risk. The AI-900 course specifies that responsible AI usage requires maintaining human oversight in public-facing communications that influence brand image.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", the term features refers to the input variables or independent variables used by a machine learning model to make predictions. These are the measurable properties or attributes of the data that influence the output (target) value.
In a supervised learning process, data is typically divided into two parts:
* Features # The input variables used by the model to learn patterns (e.g., customer age, income, credit score).
* Label (Target) # The outcome or value the model is trying to predict (e.g., whether a loan will be approved or the amount of a house price).
During training, the model uses the features to understand how input data correlates with the target output.
Once trained, the model applies the same learned relationships to predict outcomes for new, unseen data using only the features.
For example:
* In a regression model predicting house prices, features might include square footage, number of bedrooms, and location.
* In a classification model predicting loan approval, features might include applicant income, credit score, and debt ratio.
To contrast with other options:
* Dependent variables (or labels) are the outcomes the model predicts.
* Identifiers (like customer IDs) are unique values that do not help the model learn relationships and are typically excluded from features.
* Labels are the target outputs, not the inputs.
Therefore, in Azure Machine Learning and AI-900 terminology, data values used to make a prediction are called "features."

Explanation:
You can communicate with a bot by using email # No
You can communicate with a bot by using Microsoft Teams # Yes
You can communicate with a bot by using a webchat interface # Yes
These answers are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure." The Azure Bot Service allows developers to build, test, deploy, and manage intelligent chatbots that can interact with users through various channels. Channels are communication platforms or interfaces that connect users to bots. Once a bot is built and published through the Azure Bot Service, it can be connected to multiple channels such as Microsoft Teams, webchat, Skype, Facebook Messenger, Direct Line, Slack, and others.
Let's evaluate each statement:
* You can communicate with a bot by using email # NoAzure Bot Service does not support direct interaction via email as a channel. Bots are designed for real-time or conversational interactions through messaging or voice-based platforms, not asynchronous email communication.
* You can communicate with a bot by using Microsoft Teams # YesMicrosoft Teams is one of the primary channels supported by Azure Bot Service. Bots can be integrated directly into Teams to handle chat-based conversations, provide information, automate workflows, or assist users interactively within Teams.
* You can communicate with a bot by using a webchat interface # YesThe Web Chat channel is another core feature of Azure Bot Service. It allows embedding the bot into a website or web application using the Web Chat control or the Direct Line API, enabling users to chat directly from a browser interface.
In summary, Azure Bot Service supports real-time conversational interfaces like Teams and webchat, but not email.