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Microsoft.AI-900-KR.v2026-09-01.q162 모의시험 (Page 12)

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AI-900-KR 문제 51

자주 묻는 질문(FAQ) 페이지를 사용하여 QnA Maker 봇을 빌드합니다.
봇을 더욱 사용자 친화적으로 만들려면 전문적인 인사말과 기타 응답을 추가해야 합니다.
어떻게 해야 하나요?

A. 응답의 신뢰 임계값을 높입니다.

B. 능동 학습 활성화

C. 다중 턴 질문 만들기

D. 잡담 추가

정답: D

According to the Microsoft Learn module "Build a QnA Maker knowledge base", QnA Maker allows developers to create bots that answer user queries based on documents like FAQs or manuals. To make a bot more natural and conversational, Microsoft provides a "chit-chat" feature - a prebuilt, professionally written set of responses to common conversational phrases such as greetings ("Hello"), small talk ("How are you?"), and polite phrases ("Thank you").
Adding chit-chat improves user experience by making the bot sound friendlier and more human-like. It doesn' t alter the main Q & A logic but enhances the bot's tone and responsiveness.
The other options are not correct:
* A. Increase the confidence threshold makes the bot more selective in responses but doesn't add new conversational features.
* B. Enable active learning improves knowledge base accuracy over time through user feedback.
* C. Create multi-turn questions adds conversational flow for related topics but doesn't add greetings or casual dialogue.
Thus, to make the bot more personable, the correct action is to Add chit-chat.
Reference:Microsoft Learn - Add chit-chat to a QnA Maker knowledge base

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AI-900-KR 문제 52

기술 지원 티켓팅 시스템에 Text Analytics API 기능을 적용할 계획입니다.
텍스트 분석 API 기능을 적절한 자연어 처리 시나리오에 맞춰 조정합니다.
답하려면 왼쪽 열에서 해당 기능을 오른쪽 시나리오로 끌어다 놓으세요. 각 기능은 한 번, 여러 번 또는 전혀 사용되지 않을 수 있습니다.
참고: 정답 하나당 1점입니다.

정답:

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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AI-900-KR 문제 53

문장을 완성하려면 답변란에서 적절한 옵션을 선택하세요.

정답:

Explanation:

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure

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AI-900-KR 문제 54

오디오가 전혀 없는 교육용 비디오 100개가 있습니다. 각 교육용 비디오에는 대본이 있습니다. 대본을 기반으로 각 비디오에 대한 내레이션 오디오 파일을 생성해야 합니다. 어떤 유형의 작업을 사용해야 할까요?

A. 음성 인식

B. 언어 모델링

C. 음성 합성

D. 번역

정답: C

Speech synthesis, also known as text-to-speech (TTS), is the AI workload that converts written text into spoken words. In this case, the task is to generate narration audio from provided scripts for silent instructional videos.
Speech recognition performs the opposite function - it converts speech into text. Language modeling is for text understanding and prediction (e.g., GPT). Translation converts text between languages, not from text to audio.
Therefore, the most appropriate workload, according to Microsoft's AI-900 study material under the "Speech AI capabilities" section, is speech synthesis, which enables natural voice narration generation.

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AI-900-KR 문제 55

Azure Machine Learning에서 자동화된 머신 러닝(자동화된 ML)에서 모델을 결합하는 두 가지 앙상블 방법은 무엇입니까? 각 정답은 완전한 솔루션을 제시합니다.
참고: 정답 하나당 1점입니다.

A. 회귀

B. 투표

C. 분류

D. 쌓기

E. 컴퓨터 비전

정답: B,D

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