AI-900-KR 문제 11
"Contoso, Ltd.의 주가는 얼마입니까?"라는 질문에 응답하는 스마트 기기는 어떤 AI 워크로드의 예입니까?
정답: D
The question describes a smart device that can understand and respond to a spoken or written question such as, "What is the stock price of Contoso, Ltd.?" This scenario directly maps to the Natural Language Processing (NLP) workload in Microsoft Azure AI.
According to the Microsoft AI Fundamentals (AI-900) study guide and the Microsoft Learn module "Describe features of common AI workloads," NLP enables systems to understand, interpret, and generate human language. Azure AI Language and Azure Speech services are examples of NLP-based solutions.
In this case, the smart device performs several NLP tasks:
* Speech recognition - converts spoken input into text.
* Language understanding - interprets the user's intent, i.e., retrieving the stock price of a specific company.
* Response generation - formulates a meaningful answer that can be presented back as text or speech.
This process shows a full pipeline of natural language understanding (NLU) and conversational AI. It does not involve visual data (computer vision), data pattern analysis (anomaly detection), or document search (knowledge mining).
Hence, the correct AI workload is D. Natural Language Processing.
According to the Microsoft AI Fundamentals (AI-900) study guide and the Microsoft Learn module "Describe features of common AI workloads," NLP enables systems to understand, interpret, and generate human language. Azure AI Language and Azure Speech services are examples of NLP-based solutions.
In this case, the smart device performs several NLP tasks:
* Speech recognition - converts spoken input into text.
* Language understanding - interprets the user's intent, i.e., retrieving the stock price of a specific company.
* Response generation - formulates a meaningful answer that can be presented back as text or speech.
This process shows a full pipeline of natural language understanding (NLU) and conversational AI. It does not involve visual data (computer vision), data pattern analysis (anomaly detection), or document search (knowledge mining).
Hence, the correct AI workload is D. Natural Language Processing.
AI-900-KR 문제 12
문장을 올바르게 완성하는 답을 선택하세요.
정답:
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.
AI-900-KR 문제 13
계약서 스캔 이미지에서 세부 정보를 추출하려면 무엇을 사용해야 합니까?
정답: A
The correct answer is A. Azure AI Document Intelligence (previously known as Form Recognizer). This Azure Cognitive Service is specifically designed to extract structured data and key information from scanned documents, forms, and contracts using advanced Optical Character Recognition (OCR) combined with machine learning models.
According to the Microsoft Learn module "Extract data from documents with Azure AI Document Intelligence", this service enables automated data extraction from unstructured or semi-structured documents such as contracts, invoices, receipts, and purchase orders. It identifies key-value pairs, tables, and fields such as names, dates, amounts, and signatures. This makes it ideal for digitizing legal and business documents like contracts into structured formats that can be easily searched or stored in databases.
Azure AI Document Intelligence offers several model types:
* Prebuilt models for common documents (invoices, receipts, business cards, etc.).
* Custom models trained on your specific contract layouts.
* Layout model for extracting raw text, tables, and structures.
The other options are incorrect:
* B. Azure AI Immersive Reader enhances reading comprehension and accessibility but does not extract data from documents.
* C. Azure OpenAI provides natural language generation and understanding but is not used for scanning or data extraction.
* D. Azure AI Search indexes and searches textual or document content but relies on other services (like Document Intelligence) to extract the data first.
Therefore, to automatically extract details such as contract terms, names, dates, and signatures from scanned contract images, the best Microsoft AI service is A. Azure AI Document Intelligence
According to the Microsoft Learn module "Extract data from documents with Azure AI Document Intelligence", this service enables automated data extraction from unstructured or semi-structured documents such as contracts, invoices, receipts, and purchase orders. It identifies key-value pairs, tables, and fields such as names, dates, amounts, and signatures. This makes it ideal for digitizing legal and business documents like contracts into structured formats that can be easily searched or stored in databases.
Azure AI Document Intelligence offers several model types:
* Prebuilt models for common documents (invoices, receipts, business cards, etc.).
* Custom models trained on your specific contract layouts.
* Layout model for extracting raw text, tables, and structures.
The other options are incorrect:
* B. Azure AI Immersive Reader enhances reading comprehension and accessibility but does not extract data from documents.
* C. Azure OpenAI provides natural language generation and understanding but is not used for scanning or data extraction.
* D. Azure AI Search indexes and searches textual or document content but relies on other services (like Document Intelligence) to extract the data first.
Therefore, to automatically extract details such as contract terms, names, dates, and signatures from scanned contract images, the best Microsoft AI service is A. Azure AI Document Intelligence
AI-900-KR 문제 14
문장을 올바르게 완성하는 답을 선택하세요.
정답:
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", the classification technique is a type of supervised machine learning used to predict which category or class a new observation belongs to, based on patterns learned from labeled training data.
In this scenario, a banking system that predicts whether a loan will be repaid is dealing with a binary outcome-either the loan will be repaid or will not be repaid. These two possible results represent distinct classes, making this problem a classic example of binary classification. During training, the model learns from historical data containing features such as customer income, credit score, loan amount, and repayment history, along with labeled outcomes (repaid or defaulted). After training, it can classify new applications into one of these two categories.
The AI-900 curriculum distinguishes between three key supervised and unsupervised learning approaches:
* Classification: Predicts discrete categories (e.g., spam/not spam, fraud/not fraud, will repay/won't repay).
* Regression: Predicts continuous numerical values (e.g., house prices, sales forecast, temperature).
* Clustering: Groups data based on similarity without predefined labels (e.g., customer segmentation).
Since the banking problem focuses on predicting a categorical outcome rather than a continuous numeric value, it fits squarely into the classification domain. In Azure Machine Learning, such tasks can be performed using algorithms like Logistic Regression, Decision Trees, or Support Vector Machines (SVMs), all configured for categorical prediction.
Therefore, per Microsoft's official AI-900 learning objectives, a banking system predicting whether a loan will be repaid represents a classification type of machine learning problem.
AI-900-KR 문제 15
개인 디지털 사진 컬렉션에 레이블을 지정하는 모델을 만들어야 합니다.
어떤 Azure AI 서비스를 사용해야 하나요?
어떤 Azure AI 서비스를 사용해야 하나요?
정답: A
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