AI-900: Microsoft Azure AI Fundamentals Course - May 2022

About AI-900: Microsoft Azure AI Fundamentals Course - May 2022

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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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%) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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