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AI-900-KR
Microsoft.AI-900-KR.v2026-09-01.q162 모의시험 (Page 4)

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AI-900-KR 문제 11

컴퓨터 비전을 사용할 수 있는 두 가지 작업은 무엇입니까? 정답은 각각 완전한 해답을 제시합니다. 참고: 정답 하나당 1점입니다.

A. 3차원 모델을 사용하여 사진처럼 사실적인 이미지 만들기

B. 이미지의 색상 픽셀을 객체 이름에 할당

C. 이미지의 내용을 설명합니다.

D. 데이터 스트림에서 불일치 및 이상 감지

E. 수치 데이터의 시각적 표현 생성

정답: B,C

The correct answers are B. assigning the color pixels in an image to object names and C. describing the contents of an image.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of computer vision workloads on Azure," computer vision is a branch of AI that enables systems to analyze, interpret, and understand visual data from images and videos. It allows machines to identify objects, people, text, and even describe scenes automatically.
* Option B: Assigning color pixels in an image to object names represents image classification or object detection, which are key computer vision workloads. In these tasks, AI analyzes pixel patterns to determine which pixels correspond to specific objects (for example, classifying pixels as "car," "tree," or "road").
* Option C: Describing the contents of an image corresponds to image captioning, another computer vision workload. It involves using AI models trained to generate natural language descriptions of what is visible in an image, such as "A group of people sitting at a dining table." Azure's Computer Vision service provides this functionality through its "Describe Image" API.
Incorrect options:
* A. Creating photorealistic images involves generative AI and 3D modeling, not traditional computer vision.
* D. Detecting inconsistencies and anomalies in a data stream relates to anomaly detection, not computer vision.
* E. Creating visual representations of numerical data involves data visualization, not AI-driven image analysis.

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AI-900-KR 문제 12

다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.

정답:

Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service and has nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna-bot LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service

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AI-900-KR 문제 13

다음과 같은 앱이 있습니다.
* App1: 브랜드 또는 주제에 대한 대중의 인식을 이해합니다.
* App2: 음성-텍스트 변환에 욕설 필터 적용
각 앱은 무엇을 사용하나요? 답변하려면 답변 영역에서 적절한 옵션을 선택하세요.
참고: 정답 하나당 1점입니다.

정답:

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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AI-900-KR 문제 14

여러분은 소매점의 이미지를 처리하고 경쟁사의 제품을 식별하는 도구를 만들고 있습니다.
해당 솔루션에서는 사용자 정의 모델을 사용합니다.
어떤 Azure Cognitive Services 서비스를 사용해야 하나요?

A. 사용자 정의 비전

B. 양식 인식기

C. 얼굴

D. 컴퓨터 비전

정답: A

to 300 words in Explanation:
The Custom Vision service under Azure Cognitive Services is specifically designed for image classification and object detection tasks that require a custom-trained model. According to the AI-900 official study materials, Custom Vision enables developers to "build, deploy, and improve image classifiers that recognize specific objects in images based on custom data." In this question, the goal is to build a system that processes images from retail stores and identifies products of competitors. Since these are unique products that may not be part of Microsoft's pre-trained models, a custom model must be created. The Custom Vision service allows you to upload your own labeled images (e.
g., product pictures), train a model to recognize those products, and then deploy it as an API for image recognition tasks.
Other options explained:
* B. Form Recognizer is used to extract text, key-value pairs, and tables from structured or semi- structured documents like invoices or receipts. It is not suitable for object identification.
* C. Face service detects and analyzes human faces, providing attributes like age, emotion, and facial landmarks, but cannot recognize general objects like products.
* D. Computer Vision is a general-purpose image analysis service used for tagging, OCR, and scene recognition, but it uses pre-trained models. It doesn't allow for custom product identification.
Thus, based on Microsoft's guidance, the best fit for recognizing competitor products from images using a custom-trained model is A. Custom Vision.

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AI-900-KR 문제 15

Azure OpenAI GPT-3.5 모델을 사용하는 채팅 솔루션의 응답에서 더 다양한 토큰 범위를 생성하려면 어떤 매개변수를 구성해야 합니까?

A. 최대 응답

B. 지난 메시지 포함

C. 존재 페널티

D. 정지 시퀀스

정답: C

In Azure OpenAI Service, model behavior during text or chat generation is controlled by several parameters, such as temperature, max tokens, top_p, presence penalty, and frequency penalty. According to Microsoft Learn's documentation for Azure OpenAI GPT models, the presence penalty influences how likely the model is to introduce new or diverse tokens in its responses.
Specifically, the presence penalty discourages the model from repeating previously used tokens, encouraging it to explore new topics or ideas instead of sticking to existing ones. Increasing the presence penalty value typically results in more diverse and creative outputs, while lowering it makes responses more repetitive or focused.
Option analysis:
* A. Max response (Max tokens): Controls the maximum length of the generated response, not its diversity.
* B. Past messages included: Defines how much chat history the model considers for context; it doesn't affect diversity directly.
* C. Presence penalty: Encourages novelty and introduces new tokens-this is correct for increasing response variety.
* D. Stop sequence: Specifies a sequence of characters or tokens where the model should stop generating output.

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