AI-900: Azure AI Fundamentals Practice Test - Easy/Mid QU 26

AI-900: Microsoft Azure AI Fundamentals is a cutting-edge certification course designed to equip individuals with the foundational knowledge and skills needed to understand artificial intelligence (AI) concepts and how they are implemented in Microsoft Azure. This course is ideal for individuals who are looking to kickstart their career in AI or for professionals who want to enhance their existing skill set.

AI-900 course covers a wide range of topics, including AI principles, machine learning, computer vision, natural language processing, and conversational AI. Students will learn how to build, train, and deploy machine learning models using Azure Machine Learning, as well as how to use Azure Cognitive Services to add AI capabilities to their applications.

One of the key features of the AI-900 course is the comprehensive practice exam that allows students to test their knowledge and skills before taking the official certification exam. This practice exam is designed to simulate the format and difficulty level of the real exam, giving students the opportunity to identify areas where they may need to focus their study efforts.

In addition to this practice exam, the AI-900 course also includes hands-on labs and exercises that allow students to apply their knowledge in a practical setting. These labs cover a variety of topics, such as building a machine learning model, creating a computer vision application, and implementing a chatbot using Azure Cognitive Services.

AI-900 course is taught by industry experts who have extensive experience working with AI technologies and Microsoft Azure. These instructors provide valuable insights and real-world examples that help students better understand how AI is used in different industries and applications.

Upon successful completion of the AI-900 course, students will be prepared to take the official Microsoft Azure AI Fundamentals certification exam. This exam tests students on their knowledge of AI concepts and their ability to implement AI solutions using Microsoft Azure. By earning this certification, students can demonstrate their proficiency in AI technologies and increase their employment opportunities in the rapidly growing field of AI.

AI-900 : Microsoft Azure AI Fundamentals Exam details :

• Exam Name: Microsoft Certified - Azure AI Fundamentals

• Exam Code: AI-900

• Exam Price: $99 (USD)

• Number of Questions: Maximum of 40-60 questions,

• Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,

• Length of Test: 60 Minutes. The exam is available in English and Japanese languages.

• Passing Score: 700 / 1000

• Languages : English, Japanese, Korean, and Simplified Chinese

• Schedule Exam : Pearson VUE

AI-900 : Microsoft Azure AI Fundamentals Certification Exams skill questions:

Skill Measurement Exam Topics:-

• Describe Artificial Intelligence workloads and considerations (15–20%)

• Describe fundamental principles of machine learning on Azure (20–25%)

• Describe features of computer vision workloads on Azure (15–20%)

• Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

• Describe features of generative AI workloads on Azure (15–20%)

Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

• Identify features of content moderation and personalization workloads

• Identify computer vision workloads

• Identify natural language processing workloads

• Identify knowledge mining workloads

• Identify document intelligence workloads

• Identify features of generative 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 (20–25%)

Identify common machine learning techniques

• Identify regression machine learning scenarios

• Identify classification machine learning scenarios

• Identify clustering machine learning scenarios

• Identify features of deep learning techniques

Describe core machine learning concepts

• Identify features and labels in a dataset for machine learning

• Describe how training and validation datasets are used in machine learning

Describe Azure Machine Learning capabilities

• Describe capabilities of automated machine learning

• Describe data and compute services for data science and machine learning

• Describe model management and deployment capabilities in Azure Machine Learning

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 optical character recognition solutions

• Identify features of facial detection and facial analysis solutions

Identify Azure tools and services for computer vision tasks

• Describe capabilities of the Azure AI Vision service

• Describe capabilities of the Azure AI Face detection 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

• Describe capabilities of the Azure AI Language service

• Describe capabilities of the Azure AI Speech service

Describe features of generative AI workloads on Azure (15–20%)

Identify features of generative AI solutions

• Identify features of generative AI models

• Identify common scenarios for generative AI

• Identify responsible AI considerations for generative AI

Identify capabilities of Azure OpenAI Service

• Describe natural language generation capabilities of Azure OpenAI Service

• Describe code generation capabilities of Azure OpenAI Service

• Describe image generation capabilities of Azure OpenAI Service

  
  

Overall, the AI-900: Microsoft Azure AI Fundamentals course is a comprehensive and practical introduction to AI concepts and technologies. With its focus on hands-on learning and real-world applications, this course is ideal for individuals who are looking to build a solid foundation in AI and advance their career in this exciting and rapidly evolving field.