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Learning Path
All in all, Microsoft provides two ways to help candidates prepare for the Microsoft AI-900 exam. These include the free online learning option and the paid, instructor-led training. Let’s discuss the two most popular courses offered by each of these options by the vendor:
- Get Started with Artificial Intelligence
The online gratis class, “Get Started with Artificial Intelligence” features one module and is intended to run for a maximum of 34 minutes. In general, this course is designed to help you get started with Microsoft services that empower new AI solutions. There are no prerequisites associated with this training. However, it features one module with a total of 10 units including the introduction and an understanding of machine learning, anomaly detection, computer vision, natural language processing, conversational AI, and responsible AI just to mention a few. Visit the official certification page to find out more information about this learning path that also includes classes on ML, NLP, and conversational AI.
- Microsoft Azure AI Fundamentals
This one-day course is part of the instructor-led learning platform and is, therefore, a paid option. As the name suggests, it is meant to introduce the candidates to fundamental AI concepts. The training, in particular, emphasizes the skills you need to become well versed in AI workloads and understand the Azure services that support such solutions. Usually, the course curriculum is viewed as comprehensive blended learning, featuring a combination of online materials and instructor-led training to ensure candidates have a good grasp of the exam topic areas. Even more, the hands-on exercises that are featured here are meant to help learners understand the test domains in real-world scenarios. Familiarity with the internet and computer technology is not a prerequisite but a great combination that will go a long way toward helping you register excellent results in this course and the ultimate exam.
The Microsoft AI-900 exam will measure the candidates’ skills and competence in a range of topics. They are as follows:
- Explain the Fundamental Principles of ML on Azure (30-35%): The potential candidates for the Microsoft AI-900 exam should be able to identify the common types of machine learning and explain its core concepts. They also need to know how to identify the core tasks that are involved in creating the ML solutions. Additionally, they need to have the knowledge of the capabilities of no-code ML with Azure ML studio.
- Explain the Features of NLP (Natural Language Processing) Workloads Available on Azure (15-20%): This subject area will measure your ability to identify the features of basic Natural Language Processing Workload scenarios. It will also test your skills in identifying different Azure services and tools for NLP workloads. The topic will cover the understanding of the capabilities of Text Analytics service, Language Understanding service, Speech service, and Translator Text service.
- Explain AI Workloads & Considerations (15-20%): This section will measure the individuals’ ability to identify different features of common artificial intelligence workloads. It will also evaluate their competence in identifying the guiding principles that are responsible for AI.
- Explain the Features of Computer Vision Workloads Available on Azure (15-20%): This domain requires that the test takers demonstrate competence in identifying the basic categories of computer vision solutions. It will also measure their skills in identifying different Azure services and tools for computer vision tasks. You will also need an understanding of the capabilities of Computer Vision service, Custom Vision service, Face service, and Form Recognizer service.
- Explain the Features of Conversational Artificial Intelligence Workloads Available on Azure (15-20%): The applicants must demonstrate the understanding of common use cases associated with conversational artificial intelligence. This area also measures one’s knowledge of Azure services associated with conversational artificial intelligence.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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How to book the AI-900: Microsoft Azure AI Fundamentals Exam
- Step 1: Visit to Microsoft Learning and search for AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules.
- Step 2: Sign up/Login to Pearson VUE account.
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
Requisites of Microsoft AI-900 Exam
In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:
- AZ-100: Fundamentals of Azure
- AZ-200: Azure Fundamentals Assessment
Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision - Identify common computer vision tasks |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Describe Azure capabilities for NLP - Identify Azure AI services for NLP |
Microsoft Azure AI Fundamentals (AI-900 Korean Version) Sample Questions:
다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
The Azure AI Language service (part of Azure Cognitive Services) provides a set of natural language processing (NLP) capabilities designed to analyze and interpret text data. Its core features include language detection, key phrase extraction, sentiment analysis, and named entity recognition (NER).
* Language Identification - YESAccording to the Microsoft Learn module "Analyze text with Azure AI Language," one of the service's built-in capabilities is language detection, which determines the language of a given text string (e.g., English, Spanish, or French). This allows applications to automatically adapt to multilingual input.
* Handwritten Signature Detection - NOThe Azure AI Language service only processes text-based data; it does not analyze images or handwriting. Detecting handwritten signatures requires computer vision capabilities, specifically Azure AI Vision or Azure AI Document Intelligence, which can extract and interpret visual content from scanned documents or images.
* Identifying Companies and Organizations - YESThe Named Entity Recognition (NER) feature within Azure AI Language can identify entities such as people, locations, dates, organizations, and companies mentioned in text. It tags these entities with categories, enabling structured analysis of unstructured data.
# Summary:
* Language detection # Yes (supported by AI Language).
* Handwritten signatures # No (requires Computer Vision).
* Entity recognition for companies/organizations # Yes (supported by AI Language NER).
Azure Al Vision 서비스를 사용하여 어떤 작업을 수행할 수 있나요?
- A. 문서에서 핵심 구문 추출
- B. 라이브 비디오 스트림에서 동물 품종 식별
- C. 손으로 쓴 편지에서 데이터 추출
- D. 교육용 비디오 썸네일 만들기
Correct Answer: B 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
문장을 완성하려면 답변란에서 적절한 옵션을 선택하세요.
Correct Answer:
Explanation:
Reliability & Safety
https://en.wikipedia.org/wiki/Tay_(bot)
"To build trust, it ' s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It ' s also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing. We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don ' t have unexpected performance failures, and don ' t evolve in ways that are inconsistent with original expectations"
Azure Machine Learning을 사용하여 학습된 모델의 여러 버전을 추적해야 합니다. 어떻게 해야 할까요?
- A. 추론 먼지 제거기를 제공합니다.
- B. 모델을 설명하세요.
- C. 모델을 등록합니다.
- D. 훈련 데이터를 등록합니다.
Correct Answer: C 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
문장을 완성하려면 답변란에서 적절한 옵션을 선택하세요.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore natural language processing (NLP) in Azure", Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP is used to extract meaning and intent from text or speech, perform sentiment analysis, identify entities, and classify content based on context.
One of the primary applications of NLP is text classification, where an AI model automatically categorizes text documents or messages into predefined classes. Classifying emails as work-related or personal is a textbook example of this NLP capability. It involves analyzing the words, phrases, and structure of the text to determine the email's category. Microsoft Learn highlights this type of problem as document classification, an essential NLP use case often implemented through Azure Cognitive Services such as Text Analytics or Language Studio.
Let's examine why the other options are incorrect:
* Predict the number of future car rentals - This is a time series forecasting or regression task, not NLP.
* Predict which website visitors will make a transaction - This is a predictive analytics or machine learning classification problem based on behavioral data, not language understanding.
* Stop a process in a factory when extremely high temperatures are registered - This relates to IoT automation or sensor-based anomaly detection, not NLP.
Therefore, based on Microsoft's AI-900 materials, Natural Language Processing is best used for tasks involving understanding and classifying text, such as classifying email messages as work-related or personal.
This example perfectly aligns with NLP's goal-to enable machines to process and derive insights from human language inputs.
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