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Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.
Part of the requirements for: Microsoft Certified: Azure AI Fundamentals
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
| Section | Weight | Objectives |
|---|---|---|
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe machine learning pipelines - Describe automated machine learning - Describe capabilities of Azure Machine Learning |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision |
이메일, Microsoft Teams, 웹 채팅 등 다양한 채널을 통해 사용자와 소통하는 대화형 AI 솔루션을 개발하고 있습니다.
어떤 서비스를 사용해야 하나요?
Correct Answer: D 🗳️
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Azure OpenAI DALL-E 모델을 사용하여 수행할 수 있는 두 가지 작업은 무엇입니까? 각 정답은 완전한 해결책을 제시합니다.
참고: 정답은 1점입니다.
Correct Answer: C,D 🗳️
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미리 정의된 답변을 통해 간단한 질문에 답하는 챗봇을 구현하여 전화 상담원의 업무 부담을 줄여야 합니다.
목표를 달성하려면 어떤 두 가지 AI 서비스를 사용해야 할까요? 각 정답은 해결책의 일부를 제시합니다.
참고: 정답 하나당 1점입니다.
Correct Answer: B,C 🗳️
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스캔된 송장을 분석하고 청구 주소, 총 금액 등의 데이터를 추출하는 데 무엇을 사용할 수 있나요?
Correct Answer: C 🗳️
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문장을 올바르게 완성하는 답을 선택하세요.
Correct Answer:

Explanation:
The correct completion of the sentence is:
"The Form Recognizer service can be used to extract information from a driver's license to populate a database." According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of computer vision workloads," Azure Form Recognizer (part of Azure AI Document Intelligence) is a document processing service that uses machine learning and optical character recognition (OCR) to extract structured data, key-value pairs, and text from documents such as invoices, receipts, identity cards, and driver's licenses.
This service allows businesses to automate data entry and document processing workflows by converting physical or scanned documents into machine-readable formats. For example, with a driver's license, Form Recognizer can extract structured data fields such as Name, Date of Birth, License Number, and Expiration Date, and automatically populate those values into a database or CRM system.
The AI-900 study materials emphasize that Form Recognizer is designed to handle both structured and unstructured document layouts. It includes prebuilt models for common document types (like invoices, receipts, and identity documents) and supports custom models for domain-specific forms.
By comparison:
* Computer Vision extracts general text or image content but doesn't structure or label extracted fields.
* Custom Vision is used for training image classification or object detection models.
* Conversational Language Understanding is for processing text or speech to determine intent, not extracting document data.
Therefore, based on the Microsoft Learn AI-900 official study content, the Form Recognizer service is the correct choice, as it is explicitly designed to extract and structure data from documents like driver's licenses, forms, and receipts - making it ideal for automatically populating a database.
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