Passing exam with AI-901 Korean latest training materials

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Updated: Oct 28, 2026

No. of Questions: 132 Questions & Answers with Testing Engine

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Microsoft AI-901 Korean Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.
Topic 2
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.

Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901

Microsoft AI-901 Korean Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-901
Exam Format:Case studies, Multiple choice, Multiple select
Exam Price:$99 USD
Real Exam Qty:30-40
Certificate Validity Period:Lifetime
Available Languages:Russian, Portuguese (Brazil), French, German, Indonesian (Indonesia), Chinese (Traditional), Spanish, Italian, Japanese, Korean, Arabic (Saudi Arabia), English, Chinese (Simplified)
Exam Duration:60 minutes
Passing Score:700
Recommended Training:Microsoft Learn: Get started with Microsoft Foundry
Microsoft Learn: AI concepts for developers and technology professionals
Exam Registration:Microsoft Certification Exam Registration
Pearson VUE Scheduling
Sample Questions:Microsoft AI-901 Korean Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:No required prerequisites; basic understanding of cloud computing and familiarity with Python syntax recommended
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-901/

Microsoft Azure AI Fundamentals (AI-901 Korean Version) Sample Questions:

Question #1

여러분은 자연어 처리(NLP)를 사용하여 사용자의 텍스트 입력에 따라 다음과 같은 작업을 수행하는 챗봇을 구축하고 있습니다.
* 고객 주문을 받습니다
* 지원 문서 검색
* 주문 상태 업데이트를 조회합니다.
어떤 유형의 자연어 처리(NLP)를 사용해야 할까요?

  • A. 언어 이해
  • B. 감정 분석
  • C. 번역
  • D. 개체명 인식
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #2

다음 각 문장에 대해, 문장이 참이면 '아니오'를 선택하고, 그렇지 않으면 '아니오'를 선택하십시오.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Statement
Answer
Image analysis capabilities in Microsoft Foundry Tools can perform optical character recognition (OCR).
Yes
Image analysis capabilities in Microsoft Foundry Tools can generate captions and descriptive tags for images.
Yes
Image analysis capabilities in Microsoft Foundry Tools are designed primarily to create new images from text prompts.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is Yes, Yes, No.
The first statement is Yes. Azure Vision Image Analysis in Microsoft Foundry Tools supports optical character recognition (OCR). Microsoft documents Image Analysis 4.0 as supporting synchronous OCR through its Read capability, which extracts printed or handwritten text contained within images.
The second statement is also Yes. Image Analysis provides both captioning and image tagging. Captions generate a human-readable description of image content, while tags identify recognizable objects, scenes, actions, and other visual concepts. Microsoft explicitly lists capabilities such as Captions, Dense Captions, Tags, Object Detection, People detection, and Read/OCR as Image Analysis features.
The third statement is No. Image Analysis is primarily concerned with extracting information from existing images, not generating new images. Creating an image from a textual prompt is a generative AI/image- generation workload, typically performed by image-generation models. Image Analysis instead interprets visual input by extracting text, captions, tags, objects, and related information.

Question #3

다음 각 문장에 대해, 문장이 사실이면 '예'를 선택하고, 그렇지 않으면 '아니요'를 선택하십시오.
참고: 정답 하나당 1점입니다.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Statement 1: A summarization model is used to transcribe spoken audio into written text. = No Summarization creates a shorter version of existing text. Transcribing spoken audio into written text is a speech recognition / speech-to-text capability, not summarization.
Statement 2: A chat completion model can generate vector embeddings for semantic tasks. = No Chat completion models are used to generate conversational text responses. Vector embeddings for semantic search, similarity, and retrieval tasks are generated by embedding models, not chat completion models.
Statement 3: An embedding model can generate vector representations of the provided input. = Yes This is correct. Embedding models convert input such as text into vector representations that capture semantic meaning and can be used for tasks such as similarity search, clustering, and retrieval.

Question #4

Microsoft Foundry에 멀티모달 모델이 배포되어 있습니다.
이미지를 분석하고 텍스트 요약과 오디오 요약을 모두 제공하도록 모델을 구성해야 합니다.
모델에게 보내야 하는 최소 사용자 프롬프트 수는 몇 개입니까?

  • A. 1
  • B. 3
  • C. 2
  • D. A
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

당신은 Foundry Tools의 Azure Content Understanding을 사용하여 음성 메일 녹음을 분석하는 애플리케이션을 개발하고 있습니다.
녹음 파일에서 녹취록과 구조화된 정보를 추출해야 합니다.
어떤 유형의 분석기를 사용해야 할까요?

  • A. 비디오 분석기
  • B. 오디오 분석기
  • C. 이미지 분석기
  • D. 문서 분석기
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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

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