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The Microsoft AI-900: Microsoft Azure AI Fundamentals exam is aimed at those professionals who want to demonstrate their knowledge of basic Machine Learning (ML) and Artificial Intelligence (AI) workloads. The test evaluates their understanding of how to implement both ML and AI on Azure. The candidates who pass this exam are eligible for the Microsoft Certified: Azure AI Fundamentals certification.
About Exam AI-900
The interested candidate for the Microsoft AI-900 test, which also goes under the name the Azure test for AI Fundamentals, should have at least some foundational understanding of the concept of AI features alongside ML ones. This exam will test the entrant's knowledge of different AI common workloads, principles of AI and ML, NLP workloads, and other related concepts. When it comes to the details for this exam, the Microsoft AI-900 is available in several languages, among which you'll find Spanish, English, Korean, German, Chinese (Simplified), and French. Such an exam comes with a registration fee of $99 and should be scheduled through Pearson VUE. Also, in the main exam, candidates will be exposed to around 40-60 tasks with 1 hour given to solve them.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Describing AI-900 Exam Facts and Topics
In general, AI-900 is a computer-based exam that’s available in English, Chinese, Japanese, Spanish, French, German, and Korean languages. It costs $99 for every attempt and can be scheduled with Certiport or Pearson VUE. Recently, the AI-900 exam curriculum has been updated to reflect the following skills as contained in the official study guide:
- Detailing Artificial Intelligence considerations and workloads (15-20%);
- Explaining features of computer vision workloads on Azure (15-20%);
- Describing fundamental principles of machine learning on Azure (30-35%);
- Designing features of NLP (Natural Language Processing) workloads on Azure (15-20%);
- Defining features of conversational AI workloads on Azure (15-20%).
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Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe generative AI concepts |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Identify types of NLP solutions |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning - Describe machine learning pipelines |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
Microsoft Azure AI Fundamentals (AI-900 Korean Version) Sample Questions:
컴퓨터 비전 서비스를 적절한 AI 워크로드에 맞춰 조정합니다.
답변하려면 왼쪽 열에서 해당 서비스를 오른쪽 작업 부하로 끌어다 놓으세요. 각 서비스는 한 번, 여러 번 또는 전혀 사용되지 않을 수 있습니다.
참고: 정답을 맞힐 때마다 1점이 주어집니다.
Correct Answer:
Explanation:
This question evaluates understanding of the different Azure AI Computer Vision services and their distinct functionalities, as covered in the Microsoft AI-900 study guide and Microsoft Learn modules under "Describe features of common AI workloads" and "Identify Azure services for computer vision."
* Azure AI Document Intelligence (formerly known as Form Recognizer):This service is designed to extract structured information from documents, such as forms, receipts, and invoices. It uses optical character recognition (OCR) combined with AI models to detect key-value pairs, tables, and handwritten text. This makes it ideal for automating data entry and digitizing scanned documents.
Hence, it matches "Extract information from scanned forms and invoices."
* Azure AI Vision (formerly Computer Vision):This service provides image and video analysis capabilities. It can detect objects, people, text, and scenes; generate image captions; and extract descriptive tags. It also supports OCR for printed and handwritten text within images. Therefore, it matches "Analyze images and video, and extract descriptions, tags, objects, and text."
* Azure AI Custom Vision:Custom Vision allows you to train your own image classification and object detection models using your own labeled images. Unlike the general Vision service, Custom Vision lets you build domain-specific models-for example, detecting your company's products or identifying manufacturing defects. Hence, it matches "Train custom image classification and object detection models by using your own images." These three services complement each other within Azure's computer vision ecosystem, collectively supporting both general-purpose and specialized AI solutions for visual data analysis.
문장을 올바르게 완성하는 답을 선택하세요.
Correct Answer:
Explanation:
When building a K-means clustering model, all features (variables) used in the model must be numeric in nature. According to the Microsoft Azure AI Fundamentals (AI-900) study materials and standard machine learning theory, K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on their similarity - specifically by minimizing the Euclidean distance between data points and their assigned cluster centroids.
Because the K-means algorithm depends on distance calculations, it requires numeric data types. The Euclidean distance (or similar measures) can only be computed between numerical values. Therefore, all categorical or text data must first be converted into numeric form through feature engineering techniques such as one-hot encoding, label encoding, or embedding vectors, depending on the nature of the data.
Here's how K-means works in summary:
* The algorithm initializes a predefined number of centroids (K).
* Each data point is assigned to the nearest centroid based on numeric distance.
* The centroids are recalculated as the mean of the points in each cluster.
* The process repeats until convergence.
If non-numeric data (e.g., text or Boolean) were provided, the model would not be able to calculate distances accurately, leading to computational errors.
Other options are incorrect:
* Boolean and integer types can represent numeric values but are considered special cases; the algorithm requires general numeric representation (e.g., continuous values).
* Text cannot be processed directly without conversion.
Thus, according to Azure Machine Learning and AI-900 official concepts, all features in a K-means clustering model must be numeric to ensure valid mathematical operations and clustering accuracy.
Azure OpenAI 서비스를 호출하는 데 사용할 수 있는 두 가지 도구는 무엇인가요? 각 정답은 완전한 해결책을 제시합니다.
참고: 정답은 1점입니다.
- A. Python용 Azure SDK
- B. Azure 명령줄 인터페이스(CLI)
- C. JavaScript용 Azure SDK
- D. Azure REST API
Correct Answer: A,D 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
어떤 상황에서 핵심어 추출을 사용해야 할까요?
- A. 오디오 트랙을 기반으로 비디오에 대한 자막 생성
- B. 영어에서 독일어로 문서 세트 번역
- C. 동일한 주제에 대한 정보를 제공하는 문서 식별
- D. 레스토랑에 대한 리뷰가 긍정적인지 부정적인지 식별
Correct Answer: C 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
문장을 올바르게 완성하는 답을 선택하세요.
Correct Answer:
Explanation:
In Azure OpenAI Service, the temperature parameter directly controls the creativity and determinism of responses generated by models such as GPT-3.5. According to the Microsoft Learn documentation for Azure OpenAI models, temperature is a numeric value (typically between 0.0 and 2.0) that determines how
"random" or "deterministic" the output should be.
* A lower temperature value (for example, 0 or 0.2) makes the model's responses more deterministic, meaning the same prompt consistently produces nearly identical outputs.
* A higher temperature value (for example, 0.8 or 1.0) encourages creativity and variety, causing the model to generate different phrasing or interpretations each time it responds.
When a question specifies the need for more deterministic responses, Microsoft's guidance is to decrease the temperature parameter. This adjustment makes the model focus on the most probable tokens (words) rather than exploring less likely options, improving reliability and consistency-ideal for business or technical applications where consistent answers are essential.
The other parameters serve different purposes:
* Frequency penalty reduces repetition of the same phrases but does not control randomness.
* Max response (max tokens) limits the maximum length of the generated output.
* Stop sequence defines specific tokens that tell the model when to stop generating text.
Thus, the correct and Microsoft-verified completion is:
"You can modify the Temperature parameter to produce more deterministic responses from a chat solution that uses the Azure OpenAI GPT-3.5 model."
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