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.

Course last updated - May 2022

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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, Ihave 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