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

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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

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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:

The correct matching aligns directly with the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under "Identify features of Azure Machine Learning". Azure Machine Learning provides a suite of tools that serve different functions within the model development lifecycle - from creating workspaces, to training models, to automating experimentation.
* The Azure portal # Create a Machine Learning workspace.The Azure portal is a web-based graphical interface for managing all Azure resources. According to Microsoft Learn, you use the portal to create and configure the Azure Machine Learning workspace, which acts as the central environment where datasets, experiments, models, and compute resources are organized. Creating a workspace through the portal involves specifying a subscription, resource group, and region - tasks that are part of the setup stage rather than model development.
* Machine Learning designer # Use a drag-and-drop interface used to train and deploy models.The Machine Learning designer (formerly "Azure ML Studio (classic)") provides a visual, no-code/low- code interface for building, training, and deploying machine learning pipelines. The designer uses a drag-and-drop workflow where users connect modules representing data transformations, model training, and evaluation. This tool is ideal for beginners and those who want to quickly experiment with machine learning concepts without writing code.
* Automated machine learning (Automated ML) # Use a wizard to select configurations for a machine learning run.Automated ML simplifies model creation by automatically selecting algorithms, hyperparameters, and data preprocessing options. Users interact through a guided wizard (within the Azure Machine Learning studio) that walks them through configuration steps such as selecting datasets, target columns, and performance metrics. The system then iteratively trains and evaluates multiple models to recommend the best-performing one.
Together, these tools streamline the machine learning workflow:
* Azure portal for setup and resource management,
* Machine Learning designer for visual model creation, and
* Automated ML for guided, automated model selection and tuning.

Explanation:

Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date
/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics

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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.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI," Fairness is one of Microsoft's six core principles of Responsible AI. The principle of fairness ensures that AI systems treat all individuals and groups equitably, and that the models do not produce biased or discriminatory outcomes.
Bias in AI systems can occur when training data reflects existing prejudices, inequalities, or imbalances. For example, if a dataset used for a hiring model underrepresents a certain demographic group, the AI system might produce unfair recommendations. Microsoft emphasizes that AI should not reflect or reinforce bias and that developers must actively design, test, and monitor models to mitigate unfairness.
Microsoft's Six Responsible AI Principles:
* Fairness - AI systems should treat everyone equally and avoid bias.
* Reliability and safety - AI systems must operate as intended even under unexpected conditions.
* Privacy and security - AI must protect personal and business data.
* Inclusiveness - AI should empower all people and be accessible to diverse users.
* Transparency - AI systems should be understandable and their decisions explainable.
* Accountability - Humans should be accountable for AI system outcomes.
The other options do not fit this context:
* Accountability ensures human responsibility for AI decisions.
* Inclusiveness focuses on accessibility and empowering all users.
* Transparency relates to making AI systems understandable.
Therefore, the correct answer is fairness, as it directly addresses the principle that AI systems should NOT reflect biases from the datasets used to train them.

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Explanation:

The correct answer is "An embedding."
In the context of large language models (LLMs) such as GPT-3, GPT-3.5, or GPT-4, an embedding refers to a multi-dimensional numeric vector representation assigned to each word, token, or phrase. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure OpenAI embeddings, embeddings are used to represent textual or semantic meaning in a numerical form that a machine learning model can process mathematically.
Each embedding captures the semantic relationships between words. Words or tokens with similar meanings (for example, "car" and "automobile") are represented by vectors that are close together in the multi- dimensional space, while unrelated words (like "tree" and "laptop") are farther apart. This vector representation enables the model to understand context, similarity, and relationships between different pieces of text.
Embeddings are fundamental in tasks such as:
* Semantic search: Finding documents or sentences with similar meaning.
* Clustering: Grouping related concepts together.
* Recommendation systems: Suggesting similar content based on text meaning.
* Contextual understanding: Helping generative models produce coherent and context-aware text.
Option review:
* Attention: A mechanism used within transformers to focus on relevant parts of input sequences but not a representation of words.
* A completion: Refers to the generated text output from a model, not the internal representation.
* A transformer: The architecture that powers models like GPT, not the vector representation of tokens.
Therefore, the correct term for a multi-dimensional vector assigned to each word or token in a large language model (LLM) is An embedding, which represents how meaning is numerically encoded and compared within language models.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of conversational AI workloads," the correct answer is Power Virtual Agents. This service is part of the Microsoft Power Platform and is specifically designed to enable users to create intelligent chatbots without writing any code.
Power Virtual Agents (PVA) provides a no-code/low-code environment where business users and developers can collaboratively design conversational experiences. It integrates built-in natural language processing (NLP) models to understand user intent and respond intelligently to text or speech inputs. The platform's interface allows chatbot creators to design dialogues visually, connect to back-end data via Power Automate, and publish bots on websites, Teams, or other communication channels.
This approach is highlighted in Microsoft Learn as an ideal solution for organizations that want to deploy conversational bots quickly without requiring specialized AI or programming expertise. PVA automatically leverages Microsoft's language understanding models, allowing it to interpret user input and map it to predefined topics or actions.
Let's analyze the other options:
* Azure Health Bot: A specialized solution for the healthcare industry that provides prebuilt medical compliance and healthcare content. It is not a general-purpose, no-code chatbot builder.
* Microsoft Bot Framework: A developer-focused SDK for building highly customized bots through code, offering maximum flexibility but not no-code functionality.
Therefore, the most appropriate choice that "can be used to build no-code apps that use built-in natural language processing models" is Power Virtual Agents - the official Microsoft no-code chatbot solution for conversational AI workloads.

Explanation:

The correct answer is Document Intelligence.
According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft Learn documentation, the Azure AI Document Intelligence service (formerly known as Form Recognizer) is specifically designed to extract structured data from documents, including scanned invoices, receipts, forms, and business cards.
This service combines optical character recognition (OCR) with machine learning to analyze both the layout and semantic meaning of document content. When processing scanned invoices, Document Intelligence identifies and extracts fields such as invoice numbers, dates, totals, taxes, vendor names, and line-item details.
The extracted information can then be automatically imported into business systems like accounting software or databases, eliminating manual data entry and improving operational efficiency.
Here's why the other options are incorrect:
* Generative AI: Focuses on creating new content such as text, images, or code (for example, using GPT-
4 or DALL E). It is not used for structured data extraction.
* Natural Language Processing (NLP): Deals with understanding and generating human language from text-based input, not document scanning or layout interpretation.
The Document Intelligence workload excels at handling semi-structured documents where the location and format of data vary between samples. Microsoft's prebuilt models-like Invoice, Receipt, Identity Document, and Contract-simplify extraction without requiring custom training.
In summary, if the task involves extracting data from scanned invoices, the appropriate Azure AI service is Azure AI Document Intelligence, which uses AI-powered document understanding to convert unstructured document images into structured, usable data.

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