Self-paced does not mean unsupported at GuideTorrent — the Microsoft Azure AI Fundamentals (AI-901 Korean Version) bank comes with a practice engine you can tune to your own rhythm, verified answers throughout, and a support team behind every AI-901 Korean order.
Microsoft AI-901 Korean Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Price: | USD 99 |
| Exam Format: | Multiple select, Match the service to use case, Scenario-based, Drag and drop, Multiple choice |
| Exam Duration: | 45-60 |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Available Languages: | Indonesian (Indonesia), French, Chinese (Simplified), German, Portuguese (Brazil), Chinese (Traditional), Spanish, Korean, English, Italian, Japanese, Russian, Arabic (Saudi Arabia) |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Passing Score: | 700 / 1000 |
| Real Exam Qty: | 40-60 |
| Sample Questions: |  |
| Exam Way: | Online (remote proctored via Pearson VUE) or at a Pearson VUE test center |
| Pre Condition: | No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900 |
Microsoft AI-901 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) | 15-20% | - Identify Azure tools and services for NLP workloads
|
| Topic 2: Describe features of generative AI workloads on Azure (20-25%) | 20-25% | - Identify generative AI services and capabilities in Microsoft Azure
|
| Topic 3: Describe features of computer vision workloads on Azure (15-20%) | 15-20% | - Identify common types of computer vision solution
|
| Topic 4: Describe Artificial Intelligence workloads and considerations (15-20%) | 15-20% | - Identify features of common AI workloads
|
| Topic 5: Describe fundamental principles of machine learning on Azure (15-20%) | 15-20% | - Describe core machine learning concepts
|
AI-901 Korean Exam Facts and Self-Paced Study at GuideTorrent
- Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) (15-20%)
- Describe Artificial Intelligence workloads and considerations (15-20%) (15-20%)
- Describe features of computer vision workloads on Azure (15-20%) (15-20%)
Microsoft Azure AI Fundamentals (AI-901 Korean Version) Sample Questions:
귀사는 사용자에게 AI 시스템의 목적, 기능 및 한계에 대한 명확한 정보를 제공합니다. 이는 마이크로소프트의 책임 있는 AI 원칙 중 어떤 원칙에 해당하는 예시입니까?
- A. 책임성
- B. 투명성
- C. 공정성
- D. 신뢰성 및 안전성
Correct Answer: B 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
다음 각 문장에 대해, 문장이 사실이면 '예'를 선택하고, 그렇지 않으면 '아니요'를 선택하십시오.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
Statement 1: Fairness can be achieved by focusing solely on improving the overall accuracy of an AI model. = No Overall accuracy alone does not guarantee fairness. A model can have high overall accuracy while still producing biased or lower-quality outcomes for specific demographic groups. Fairness requires evaluating and reducing differences in model behavior across groups.
Statement 2: Evaluating AI system outputs to identify and reduce bias across demographic groups supports fairness. = Yes This statement is correct. The Microsoft responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias. Evaluating outputs across demographic groups helps identify and reduce bias.
Statement 3: Ensuring fairness for an AI system means that all users always receive the same output from the system. = No Fairness does not mean identical outputs for all users. It means the system should avoid unfair bias and provide equitable treatment. Different users may receive different outputs when differences are relevant and appropriate.
Microsoft Foundry에서 특정 역할과 동작을 따르는 AI 에이전트를 생성해야 합니다.
무엇을 설정해야 할까요?
- A. 온도
- B. 분당 토큰 수(TPM)
- C. 최대 완료 토큰
- D. 시스템 지침
Correct Answer: D 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
설명과 해당하는 Microsoft Foundry 도구를 연결하세요.
답변하려면 왼쪽 열에서 적절한 설명을 오른쪽 도구로 드래그하세요. 각 설명은 한 번, 여러 번 또는 전혀 사용하지 않을 수 있습니다.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
Speech recognition is used to recognize spoken language and convert it into written text. Therefore, the correct match is:
Speech recognition = Transcribes spoken input
Speech synthesis, also known as text to speech, is used to convert text into spoken audio. Therefore, the correct match is:
Speech synthesis = Generates spoken output
The other options are incorrect for these two tools:
Extracts entities from the output is a text analytics / language processing task.
Translates spoken input is speech translation.
Translates written output is text translation.
문장을 올바르게 완성하는 답을 고르시오.
Correct Answer:
Explanation:
The correct answer is API endpoint. After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.
An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference.
A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.
This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.
Viewing page 1 out of 27 pages.
Viewing questions 1-5 out of 132 questions.
