AI-900: Microsoft Azure AI Fundamentals Video Course + Ques

AI-900 Exam: Microsoft Azure AI Fundamentals || 8+ hours of videos || 100% Syllabus || 2 Practice Tests || PPTs || Demos

Should you take AI-900 Exam?

What you’ll learn

  • Foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services..
  • Common ML and AI workloads and how to implement them on Azure..
  • AI workloads and considerations: AI-900 Exam.
  • Principles of machine learning on Azure: AI-900 Exam.
  • Computer vision workloads on Azure: AI-900 Exam.
  • Natural Language Processing (NLP) workloads on Azure: AI-900 Exam.
  • Conversational AI workloads on Azure: AI-900 Exam.

Course Content

  • Introduction –> 4 lectures • 10min.
  • Azure Portal Introduction: For Begineers –> 4 lectures • 27min.
  • AI workloads and considerations (15-20%) –> 17 lectures • 1hr 19min.
  • Fundamental principles of Machine Learning on Azure (30- 35%) –> 15 lectures • 3hr 1min.
  • Describe features of computer vision workloads on Azure (15-20%) –> 15 lectures • 1hr 49min.
  • Natural Language Processing (NLP) workloads on Azure (15-20%) –> 13 lectures • 1hr 24min.
  • Conversational AI workloads on Azure (15-20%) –> 6 lectures • 46min.
  • Practice Tests –> 4 lectures • 8min.
  • Wrapping up –> 1 lecture • 1min.

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Requirements

Should you take AI-900 Exam?

Artificial intelligence and machine learning are all set to dictate the future of technology. The focus of Microsoft Azure on machine-learning innovation is one of the prominent reasons for the rising popularity of Azure AI. Therefore, many aspiring candidates are looking for credible approaches for the AI-900 exam preparation that is a viable instrument for candidates to start their careers in Azure AI.

The interesting fact about the AI-900 certification is that it is a fundamental-level certification exam. Therefore, candidates from technical as well as ones with non-technical backgrounds can pursue the AI-900 certification exam. In addition, there is no requirement for software engineering or data science experience for the AI-900 certification exam.

The AI-900 certification can also help you build the foundation for Azure AI Engineer Associate or Azure Data Scientist Associate certifications.

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What includes in this course?

  • 8+ hrs. of content, Practice test, quizzes, etc.
  • PPT, Demo resources, and other study material
  • Full lifetime access
  • Certificate of course completion
  • 30-days Money-Back Guarantee
  • This course has more than enough practice questions to get you to prepare for the exam.
  • Even though there are no labs in the exam, I have practically demonstrated concepts wherever possible to make sure you feel confident with concepts.

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Exam Format and Information

Exam Name Exam AI-900: Microsoft Azure AI Fundamentals

Exam Duration 60 Minutes

Exam Type Multiple Choice Examination

Number of Questions 40 – 60 Questions

Exam Fee $99

Eligibility/Pre-requisite None

Exam validity 1 year

Exam Languages English, Japanese, Korean, and Simplified Chinese

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The AI-900 exam covers the following topics:

  • Describe AI workloads and considerations (15-20%)
  • Describe fundamental principles of machine learning on Azure (30-35%)
  • 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 conversational AI workloads on Azure (15-20%)

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Exam Topics in detail

Domain 1: Describing AI workloads and considerations

The subtopics in this domain include,

  • Identification of features in common AI workloads
  • Identification of guiding principles for responsible AI

Domain 2: Describing fundamental principles of machine learning on Azure

The subtopics in this domain include,

  • Identification of common machine learning variants
  • Description of core machine learning concepts
  • Identification of core risks in the creation of a machine learning solution
  • Description of capabilities of no-code machine learning with Azure Machine Learning

Domain 3: Description of features in computer vision workloads on Azure

The subtopics in this domain include,

  • Identification of common types of computer vision solutions
  • Identification of Azure tools and services for computer vision tasks

Domain 4: Describing features of Natural Language Processing (NLP) workloads on Azure

The subtopics in this domain are as follows,

  • Identification of features in common NLP workload scenarios
  • Identifying Azure tools and services for NLP workloads

Domain 5: Description of features of conversational AI workloads on Azure

The subtopics in this domain include,

  • Identification of common use cases for conversational AI
  • Identifying Azure services for conversational AI

 

Happy Learning!!

Eshant Garg

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