Try and practice the latest Microsoft : AI-900 Korean real questions & answers

문장을 올바르게 완성하는 답을 선택하세요.

Explanation:

"Optical Character Recognition (OCR) extracts text from handwritten documents." According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of computer vision workloads," Optical Character Recognition (OCR) is a computer vision capability that enables AI systems to detect and extract printed or handwritten text from images, scanned documents, and photographs.
Microsoft Learn explains that OCR uses machine learning algorithms to analyze visual data, locate regions containing text, and then convert that text into machine-readable digital format. This capability is essential for automating processes such as document digitization, form processing, and data extraction.
OCR technology is provided through services such as the Azure Cognitive Services Computer Vision API and Azure Form Recognizer. The Computer Vision API's OCR feature can extract text from both typed and handwritten sources, including receipts, invoices, letters, and forms. Once extracted, this text can be processed, searched, or stored electronically, enabling automation and efficiency in document management systems.
Let's review the incorrect options:
* Object detection identifies and locates objects in an image by drawing bounding boxes (e.g., detecting vehicles or people).
* Facial recognition identifies or verifies individuals by comparing facial features.
* Image classification assigns an image to one or more predefined categories (e.g., "dog," "car," "tree").
None of these perform the task of extracting textual content from images - that is uniquely handled by Optical Character Recognition (OCR).
Therefore, based on the AI-900 official study content, the verified and correct answer is Optical Character Recognition (OCR), as it specifically extracts text (printed or handwritten) from image-based documents.

문장을 올바르게 완성하는 답을 선택하세요.

Explanation:
When building a K-means clustering model, all features (variables) used in the model must be numeric in nature. According to the Microsoft Azure AI Fundamentals (AI-900) study materials and standard machine learning theory, K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on their similarity - specifically by minimizing the Euclidean distance between data points and their assigned cluster centroids.
Because the K-means algorithm depends on distance calculations, it requires numeric data types. The Euclidean distance (or similar measures) can only be computed between numerical values. Therefore, all categorical or text data must first be converted into numeric form through feature engineering techniques such as one-hot encoding, label encoding, or embedding vectors, depending on the nature of the data.
Here's how K-means works in summary:
* The algorithm initializes a predefined number of centroids (K).
* Each data point is assigned to the nearest centroid based on numeric distance.
* The centroids are recalculated as the mean of the points in each cluster.
* The process repeats until convergence.
If non-numeric data (e.g., text or Boolean) were provided, the model would not be able to calculate distances accurately, leading to computational errors.
Other options are incorrect:
* Boolean and integer types can represent numeric values but are considered special cases; the algorithm requires general numeric representation (e.g., continuous values).
* Text cannot be processed directly without conversion.
Thus, according to Azure Machine Learning and AI-900 official concepts, all features in a K-means clustering model must be numeric to ensure valid mathematical operations and clustering accuracy.

문장을 올바르게 완성하는 답을 선택하세요.

Explanation:
Azure Kubernetes Service (AKS).
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure Machine Learning, the Azure Kubernetes Service is commonly used to host and deploy machine learning models, including Automated ML models, into production environments. Once a model is trained using Azure Machine Learning (Azure ML), it must be deployed as a web service endpoint so it can receive data and return predictions.
Azure ML offers two primary options for hosting and deploying models:
* Azure Kubernetes Service (AKS) - for high-scale, production-grade deployments that require fast response times, high availability, and scalability.
* Azure Container Instances (ACI) - for testing or low-scale workloads where cost and simplicity are more important than performance.
AKS provides a managed Kubernetes cluster that allows for automated container orchestration, load balancing, scaling, and monitoring of deployed machine learning models. When you use Automated ML in Azure ML Studio, the generated model can be containerized and deployed directly to AKS, making it accessible as a REST API endpoint. This enables applications, systems, or users to send data and receive predictions in real time.
The other options serve different purposes:
* Azure Data Factory is used for data integration and pipeline orchestration, not model hosting.
* Azure Automation focuses on automating administrative tasks and runbooks, not ML deployment.
* Azure Logic Apps is used to automate workflows and integrate services, not to serve ML models.
Therefore, the correct service to host automated machine learning (AutoML) models in production is Azure Kubernetes Service (AKS), as it provides a reliable, scalable, and secure environment for real-time inference and enterprise AI workloads.

계약서 스캔 이미지에서 세부 정보를 추출하려면 무엇을 사용해야 합니까?

• A. Azure AI 문서 인텔리전스
• B. Azure OpenAI
• C. Azure Al 검색
• D. Azure Al 몰입형 리더

Explanation: Only visible for Actual4Cert members. You can sign-up / login (it's free).

간단한 사용자 질의에 답변하는 데 도움이 되는 비즈니스 챗봇용 콘텐츠를 제공해야 합니다.
QnA Maker를 사용하여 질문과 답변 텍스트를 만드는 세 가지 방법은 무엇인가요? 각 정답은 완전한 해결책을 제시합니다.
참고: 정답 하나당 1점입니다.

• A. 미리 정의된 데이터 소스에서 잡담 콘텐츠를 가져옵니다.
• B. 자동화된 머신 러닝을 사용하여 질문이 포함된 파일을 기반으로 모델을 학습합니다.
• C. 기존 웹페이지에서 질문과 답변을 생성합니다.
• D. 질문과 답변을 직접 입력하세요.
• E. 봇을 Cortana 채널에 연결하고 Cortana를 사용하여 질문하세요.

Explanation: Only visible for Actual4Cert members. You can sign-up / login (it's free).