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Microsoft AI-900 Exam Topics, Blueprint and Syllabus

Microsoft Azure AI Fundamentals

Last Update December 12, 2024
Total Questions : 276

Our Microsoft Certified: Azure AI Fundamentals AI-900 exam questions and answers cover all the topics of the latest Microsoft Azure AI Fundamentals exam, See the topics listed below. We also provide Microsoft AI-900 exam dumps with accurate exam content to help you prepare for the exam quickly and easily. Additionally, we offer a range of Microsoft AI-900 resources to help you understand the topics covered in the exam, such as Microsoft Certified: Azure AI Fundamentals video tutorials, AI-900 study guides, and AI-900 practice exams. With these resources, you can develop a better understanding of the topics covered in the exam and be better prepared for success.

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Microsoft AI-900 Exam Overview :

Exam Name Microsoft Azure AI Fundamentals
Exam Code AI-900
Actual Exam Duration The duration of the Microsoft AI-900 exam is approximately 60 minutes.
What exam is all about The Microsoft AI-900 exam is an entry-level certification exam that tests the candidate's knowledge of artificial intelligence (AI) and machine learning (ML) concepts and their applications in Microsoft Azure. The exam covers topics such as AI workloads and considerations, fundamental principles of machine learning, data science processes, and Azure AI services. Passing this exam demonstrates the candidate's understanding of AI and ML concepts and their ability to use Azure AI services to build intelligent solutions.
Passing Score required The passing score required in the Microsoft AI-900 exam is 700 out of 1000. This means that you need to answer at least 70% of the questions correctly to pass the exam. The actual passing score may vary depending on the difficulty level of the exam. It is recommended to aim for a higher score to ensure a better chance of passing the exam.
Competency Level required I do not have personal experience or knowledge about competency levels required for Microsoft AI-900 Exam. However, according to Microsoft, the AI-900 exam is designed for individuals who want to demonstrate foundational knowledge of Microsoft's AI solutions and services. It is an entry-level exam that does not require any specific prerequisites or technical background. Candidates should have a basic understanding of AI concepts, including machine learning, natural language processing, computer vision, and conversational AI. They should also be familiar with Microsoft's AI services, such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service.
Questions Format The Microsoft AI-900 exam consists of multiple-choice questions, drag and drop questions, and scenario-based questions. The exam is designed to test the candidate's knowledge and understanding of fundamental concepts related to artificial intelligence and machine learning. The exam also evaluates the candidate's ability to apply these concepts to real-world scenarios and solve problems using AI and ML technologies.
Delivery of Exam The Microsoft AI-900 exam is a multiple-choice exam that is delivered online through the Microsoft Learning platform.
Language offered English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian
Cost of exam $99 USD (Price based on the country or region in which the exam is proctored.)
Target Audience The Microsoft AI-900 certification exam is designed for individuals who want to demonstrate their foundational knowledge of artificial intelligence (AI) and its applications. The target audience for this exam includes: 1. Business leaders and decision-makers who want to understand the potential of AI and how it can be used to drive business outcomes. 2. Developers who want to learn how to build AI solutions using Microsoft technologies. 3. Data scientists who want to expand their knowledge of AI and its applications. 4. IT professionals who want to understand how AI can be integrated into their organization's infrastructure. 5. Students and educators who want to learn about AI and its potential impact on society. Overall, the AI-900 exam is suitable for anyone who wants to gain a foundational understanding of AI and its applications, regardless of their technical background or job role.
Average Salary in Market I do not have access to real-time salary data. However, according to Payscale, the average salary for an AI engineer with Microsoft AI-900 certification is around $110,000 per year in the United States. However, the salary may vary depending on the location, industry, and experience level of the individual.
Testing Provider I cannot provide the Microsoft AI-900 exam for testing. However, you can visit the Microsoft website to register for the exam and find authorized testing centers near you.
Recommended Experience I do not have personal experience, but I can provide you with the recommended experience for the Microsoft AI-900 exam. The Microsoft AI-900 exam is designed for individuals who want to demonstrate their foundational knowledge of AI and its business applications. The recommended experience for this exam includes: 1. Familiarity with basic programming concepts and algorithms 2. Understanding of data analysis and visualization techniques 3. Knowledge of machine learning concepts and techniques 4. Understanding of natural language processing (NLP) and computer vision 5. Familiarity with Azure services and tools related to AI, such as Azure Machine Learning, Cognitive Services, and Bot Services. It is also recommended that candidates have some experience working with data and analytics, as well as some understanding of cloud computing concepts. However, these are not mandatory requirements for taking the exam.
Prerequisite According to Microsoft's official website, there are no specific prerequisites for the AI-900 exam. However, it is recommended that candidates have a basic understanding of cloud computing and data science concepts. Additionally, candidates should have some familiarity with Microsoft Azure and its services.
Retirement (If Applicable) Microsoft usually provides advance notice before retiring any certification exam. It is recommended to check the Microsoft website or contact their support team for the latest information on the retirement date of the AI-900 exam.
Certification Track (RoadMap): The Microsoft AI-900 exam is a certification exam that tests the candidate's knowledge and skills in the field of Artificial Intelligence (AI). The certification track or roadmap for the AI-900 exam includes the following steps: 1. Understanding AI Fundamentals: This is the first step in the certification track, where the candidate learns the basics of AI, including its history, applications, and key concepts. 2. Preparing for the AI-900 Exam: The next step is to prepare for the AI-900 exam by studying the exam objectives, taking practice tests, and reviewing study materials. 3. Passing the AI-900 Exam: The third step is to pass the AI-900 exam, which tests the candidate's knowledge of AI concepts, tools, and technologies. 4. Advanced AI Certifications: After passing the AI-900 exam, the candidate can pursue advanced AI certifications, such as the AI-100, DP-100, and DP-200 exams, which focus on specific AI technologies and applications. 5. Continuing Education: Finally, the candidate must continue to stay up-to-date with the latest AI technologies and trends by attending conferences, taking courses, and participating in online communities.
Official Information https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
See Expected Questions Microsoft AI-900 Expected Questions in Actual Exam
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Microsoft AI-900 Exam Topics :

Section Weight Objectives
Describe Artificial Intelligence workloads and considerations 15-20% Identify features of common AI workloads
  • identify prediction/forecastingworkloads
  • identify features of anomaly detection workloads
  • identify computer vision workloads
  • identify natural language processing or knowledge mining workloads
  • identify conversational AI workloads

Identify guiding principles for responsible AI
  • describe considerations for fairness in an AI solution
  • describe considerations for reliability and safety in an AI solution
  • describe considerations for privacy and security in an AI solution
  • describe considerations for inclusiveness in an AI solution
  • describe considerations for transparency in an AI solution
  • describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure 30-35% Identify common machine learning types
  • identify regression machine learning scenarios
  • identify classification machine learning scenarios
  • identify clustering machine learning scenarios
Describe core machine learning concepts
  • identify features and labels in a dataset for machine learning
  • describe how training and validation datasets areused in machine learning
  • describe how machine learning algorithms are used for model training
  • select and interpret model evaluation metrics for classification and regression
Identify core tasks in creating a machine learning solution
  • describe common features of data ingestion and preparation
  • describe feature engineering and selection
  • describe common features of model training and evaluation
  • describe common features of model deployment and management
Describe capabilities of no-code machine learning with Azure Machine Learning studio
  • automated ML UI
  • azure Machine Learning designer
Describe features of computer vision workloads on Azure 15-20% Identify common types of computer vision solution:
  • identify features of image classification solutions
  • identify features of object detection solutions
  • identify features of semantic segmentation solutions
  • identify features of optical character recognition solutions
  • identify features of facial detection, facial recognition, and facial analysis solutions
Identify Azure tools and services for computer vision tasks
  • identify capabilities of the Computer Vision service
  • identify capabilities of the Custom Vision service
  • identify capabilities of the Face service
  • identify capabilities of the Form Recognizer service
Describe features of Natural Language Processing (NLP) workloads on Azure 15-20% Identify features of common NLP Workload Scenarios
  • identify features and uses for key phrase extraction
  • identify features and uses for entity recognition
  • identify features and uses for sentiment analysis
  • identify features and uses for language modeling
  • identify features and uses for speech recognition and synthesis
  • identify features and uses for translation
Identify Azure tools and services for NLP workloads
  • identify capabilities of the Text Analytics service
  • identify capabilities of the Language Understanding service (LUIS)
  • identify capabilities of the Speech service
  • identify capabilities of the Translator Text service
Describe features of conversational AI workloads on Azure 15-20% Identify common use cases for conversational AI
  • identify features and uses for webchat bots
  • identify features and uses for telephone voice menus
  • identify features and uses for personal digital assistants
  • identify common characteristics of conversational AI solutions
Identify Azure services for conversational AI
  • identify capabilities of the QnA Maker service
  • identify capabilities of the Azure Bot service