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

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Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Prepare data for machine learning", feature engineering refers to the process of transforming raw data into meaningful features that can be effectively used by machine learning algorithms. This includes steps such as scaling, normalization, encoding categorical variables, handling missing values, and creating new features derived from existing ones.
The question states:
"Ensuring that the numeric variables in training data are on a similar scale." This directly describes a data normalization or standardization step, which is a core component of feature engineering. The purpose of scaling numeric variables is to ensure that all features contribute equally to the model's learning process. Without normalization, features with large numeric ranges (such as "income in dollars") could dominate smaller-scale features (like "age in years"), leading to biased model performance.
In Azure Machine Learning, this is typically done using the Normalize Data module or transformations in the data preparation stage. Microsoft Learn explains that normalization and feature scaling are applied before model training to ensure that gradient-based algorithms (such as regression or neural networks) converge more efficiently and produce more accurate results.
The other options are not correct:
* Data ingestion refers to collecting and importing data into a system.
* Feature selection involves choosing the most relevant features, not scaling them.
* Model training is the phase where the algorithm learns patterns from the processed data, which occurs after feature engineering.
Therefore, ensuring that numeric variables are on a similar scale is a step in Feature Engineering.

Explanation:

The correct mapping is based on how each Azure Cognitive Service functions within the Microsoft AI ecosystem, as detailed in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Cognitive Services documentation.
* Convert spoken requests into text # Azure AI SpeechThe Azure AI Speech service provides speech-to- text (STT) capabilities, which enable an application to recognize spoken language and convert it into written text. This functionality is foundational in voice-enabled applications like digital assistants or transcription services. When a user speaks, this service captures the audio signal and produces an accurate textual representation that can then be processed by other AI services.
* Identify the intent of a user's requests # Azure AI LanguageThe Azure AI Language service (which includes Conversational Language Understanding, formerly LUIS) is designed to extract meaning from text. It identifies intents-the goals or actions a user wants to perform-and entities, which are key details within that request. For example, in the command "Book a flight to Paris," the intent is "book a flight," and the entity is "Paris."
* Apply intent to entities and utterances # Azure AI LanguageAgain, the Language service performs this deeper contextual analysis. It not only identifies what the user wants (intent) but also applies it to utterances (specific user expressions) and entities (data elements extracted from text). This helps conversational AI systems take meaningful actions, such as fulfilling user requests.
In summary, Azure AI Speech handles audio-to-text conversion, while Azure AI Language performs natural language understanding, mapping intents and entities-a workflow essential in intelligent conversational applications.

Explanation:

The correct answers are Yes, Yes, and Yes.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn content in the section "Describe features of conversational AI workloads on Azure", bots created using Azure Bot Service can interact with users across multiple channels. The AI-900 syllabus explains that Azure Bot Service integrates with various communication platforms, allowing developers to build a single bot that can be deployed in many contexts without rewriting the logic.
* "You can communicate with a bot by using Cortana." - Yes.The AI-900 learning materials explain that Cortana, Microsoft's intelligent personal assistant, can serve as a channel for bots built with the Azure Bot Service. Through the Bot Framework, bots can be connected to Cortana to allow users to interact via voice or text. Although Cortana is less prominent now, it remains conceptually included in the AI-
900 coverage as an example of a voice-based conversational AI channel.
* "You can communicate with a bot by using Microsoft Teams." - Yes.This statement is true and directly referenced in the AI-900 syllabus. Microsoft Teams is a fully supported communication channel for Azure Bot Service. Bots in Teams can handle chat messages, commands, and interactions in team or personal contexts. The Microsoft Learn materials specify Teams as one of the native connectors where enterprise users can interact with organizational bots.
* "You can communicate with a bot by using a webchat interface." - Yes.This is also true. The Web Chat channel is one of the most common ways to deploy bots publicly. Azure Bot Service provides a Web Chat control that can be embedded directly into a webpage or web application. This allows users to interact with the bot using a chat window, just like on customer service websites.
Therefore, all three interfaces-Cortana (voice-based), Microsoft Teams (enterprise chat), and Web Chat (browser-based)-are valid and officially supported communication channels for Azure bots.

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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:
facial analysis.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of computer vision workloads on Azure," facial analysis is a computer vision capability that detects faces and extracts attributes such as facial expressions, emotions, pose, occlusion, and image quality factors like exposure and noise. It does not identify or verify individual identities; rather, it interprets facial features and image characteristics to analyze conditions in an image.
In this question, the AI solution helps photographers take better portrait photos by providing feedback on exposure, noise, and occlusion - tasks directly linked to facial analysis. The model analyzes the detected face to determine if the image is well-lit, clear, and unobstructed, thereby improving photo quality. These capabilities are part of the Azure Face service in Azure Cognitive Services, which includes both facial detection and facial analysis functionalities.
Here's how the other options differ:
* Facial detection only identifies that a face exists in an image and provides its location using bounding boxes, without further interpretation.
* Facial recognition goes a step further - it attempts to identify or verify a person's identity by comparing the detected face with stored images. This is not what the scenario describes.
Thus, when an AI solution evaluates image quality aspects like exposure, noise, and occlusion, it's performing facial analysis, which focuses on understanding image and facial characteristics rather than identification.
In summary, based on Microsoft's AI-900 study material, this scenario demonstrates facial analysis, a subcategory of computer vision tasks within Azure Cognitive Services.

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Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn documentation on GitHub Copilot, the GitHub Copilot extension for Microsoft Visual Studio Code (VS Code) is powered by the OpenAI Codex model, a specialized descendant of the GPT (Generative Pre-trained Transformer) family of models. The OpenAI Codex model is specifically fine-tuned for programming tasks- it understands and generates code in multiple programming languages such as Python, JavaScript, C#, and more.
GitHub Copilot functions as an AI-powered coding assistant that suggests complete lines or blocks of code, helps write comments, and can even generate functions based on natural language prompts entered by the developer. When a user types a comment like "// sort a list of numbers in ascending order," Copilot uses the Codex model to understand the intent and generate an appropriate code implementation.
The integration with Visual Studio Code allows developers to work efficiently without needing to switch between documentation and their coding environment. GitHub Copilot leverages context-aware suggestions, meaning it understands the surrounding code, function definitions, and variable names to provide relevant completions.
The other options are incorrect because:
* GitHub source control manages code repositories but doesn't use AI models.
* IntelliSense is a built-in VS Code feature for code completion but doesn't rely on OpenAI models.
* Microsoft 365 Copilot uses large language models (like GPT-4) for productivity tools such as Word, Excel, and Outlook-not for code generation.
Therefore, the correct choice that uses the OpenAI Codex model in Visual Studio Code is GitHub Copilot, aligning with Microsoft's AI-900 learning objectives under "Identify common use cases for Azure OpenAI and GitHub Copilot."

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft's Responsible AI guidelines, customers must obtain approval based on their intended usage before accessing and deploying Azure OpenAI Service. This requirement ensures that Microsoft upholds its commitment to Responsible AI principles, which include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
The Azure OpenAI Service provides access to powerful language models such as GPT series and Codex, which can generate, summarize, and understand natural language and code. Because of the potential for misuse-such as generating harmful content, misinformation, or unethical automation-Microsoft enforces a use case review and approval process before granting customers access to the service. This process involves submitting an application describing the intended purpose, deployment method, and compliance measures.
Only after Microsoft validates that the proposed use aligns with responsible AI practices will access be approved.
This aligns with Microsoft's documented commitment that "customers are required to submit an application that describes their intended use of the Azure OpenAI Service," ensuring that all deployments follow ethical and legal standards. This approval step helps maintain transparency and prevent harmful or non-compliant use cases such as deepfake generation, biased automation, or malicious chatbot deployment.
Other options listed in the question are incorrect:
* Commit to a minimum level of expenditure - Microsoft does not require financial commitments for ethical approval.
* Pay an upfront fee - Payment is handled through normal Azure billing, not a special fee.
* Provide credit card details - Not a responsible AI requirement; this is standard for any Azure subscription.
Therefore, the correct and verified answer per Microsoft's Responsible AI framework and Azure AI-900 study