다운로드 Microsoft : AI-900日本語 질문 & 답 PDF & Test Software

정답:

Explanation:

"Generative AI enables software applications to generate new content, such as language dialogs and images."
- YES
This statement is true. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, Generative AI refers to systems capable of creating new content such as text, audio, images, video, and code. Models like GPT, DALL E, and Codex use deep learning to generate human- like responses, natural conversations, or creative media. This is a key differentiator between generative and discriminative AI - generative AI produces new data, while discriminative AI categorizes or analyzes existing data.
"The difference between a large language model (LLM) and a small language model (SLM) is the number of variables in the model." - YES This statement is true. The primary distinction between an LLM and an SLM lies in the scale of parameters (variables) within the neural network. LLMs contain billions or even trillions of parameters, which enable them to capture complex linguistic patterns and perform broader tasks. SLMs have fewer parameters, making them faster but less capable of handling complex, context-rich tasks.
"Generative AI is a type of supervised learning." - NO
This statement is false. Generative AI models are typically trained using unsupervised or self-supervised learning methods. They learn by predicting missing or next elements in large text or image datasets rather than relying on labeled input-output pairs, which are used in supervised learning.

정답: A  🗳️

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정답:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common AI workloads", OCR (Optical Character Recognition) is a Computer Vision technology that detects and extracts printed or handwritten text from images and scanned documents.
OCR allows organizations and individuals to convert physical or image-based text into machine-readable, editable, and searchable digital text.
In the context of this question, a historian working with old newspaper articles or archival documents would use OCR to digitize printed content. For instance, the historian can scan or photograph old newspaper pages, and then use an OCR tool-such as Azure Computer Vision's OCR API-to automatically recognize and extract the textual content from those images. This process enables the historian to store, edit, and analyze the content digitally without manually typing everything.
OCR works by using deep learning algorithms trained on thousands of text samples. The system analyzes patterns, shapes, and spatial relationships of characters to identify text accurately, even from low-quality or aged paper documents. Once extracted, the digital text can be indexed, translated, or processed further using Natural Language Processing (NLP) tools for content analysis.
Now, addressing the other options:
* Facial analysis is used to detect emotions, age, or gender from human faces-irrelevant to text digitization.
* Image classification identifies entire images by categories (e.g., cat, car, flower).
* Object detection identifies and locates multiple objects within an image but doesn't extract text.
Therefore, per the AI-900 learning objectives under the Computer Vision workload, the correct and verified completion is:

정답:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of common AI workloads," an anomaly detection workload is designed to identify data points or patterns that deviate significantly from what is expected or normal. These anomalies often indicate irregularities, faults, or potential issues that require attention.
In this scenario, the AI system monitors temperature data from a large machine. Normally, the machine operates within a predictable temperature range. When the AI detects sudden or unexpected temperature spikes or drops - behavior that does not match the historical pattern - it flags these occurrences as anomalies. This type of workload is fundamental in predictive maintenance and industrial monitoring, where it helps detect equipment failures, safety hazards, or energy inefficiencies before they escalate.
Microsoft's AI-900 curriculum emphasizes that anomaly detection workloads are often used in:
* Industrial IoT systems (detecting abnormal sensor readings or machine behavior)
* Finance (fraud detection or unusual transaction monitoring)
* Cybersecurity (detecting irregular network traffic or access patterns)
* Operations (identifying abnormal variations in production data)
The Azure service used for this purpose is Azure Anomaly Detector, part of Azure Cognitive Services, which uses advanced statistical and machine learning models to automatically detect outliers in time-series data such as temperature, pressure, or transaction logs.
By comparison:
* Computer vision handles image or video analysis.
* Knowledge mining extracts insights from large document collections.
* Natural Language Processing (NLP) interprets human language.
Thus, based on the official Microsoft AI-900 study guide and Microsoft Learn, the correct and verified answer is An anomaly detection workload, since detecting unusual temperature fluctuations precisely fits this AI workload type.

정답: A,B  🗳️

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