As an artificial intelligence enthusiast, if you are willing to progress your career with Microsoft Azure AI Fundamentals AI-900 Certification Exam then you are in the right place. Azure’s suite of AI services is empowering developers across the world to build chatbots, computer vision, predictive engines and digitally transform today’s businesses. This is the right time now to get your certifications done and upskill your caliber as a Microsoft Azure AI professional.

Learning Objectives

• Describe AI workloads and considerations.

• Describe fundamental principles of machine learning on Azure.

• Describe features of computer vision workloads on Azure.

• Describe features of Natural Language Processing (NLP) workloads on Azure.

• Describe features of conversational AI workloads on Azure.

Intended Audience

• Both technical and non-technical people want to demonstrate their knowledge of how to implement common machine learning and artificial intelligence workloads on Azure.

• People preparing for Microsoft’s AI-900 exam.

Prerequisites

• General programming knowledge would be helpful but is not required.

Exam Format

In order to know the difficulty level, one should know the basic details of the exam.

• So, the Microsoft Azure AI Fundamentals (AI-900) exam consists of 40-60 questions.

• Also, the types of questions that candidates can face in the exam include scenario-based single answer questions, multiple-choice questions, arrange in the correct sequence type questions, drag & drop questions, mark review, drag, and drop, etc.

• However, to pass the exam, a candidate has to score a minimum of 700 or more.

• Furthermore, to apply for the exam, the examination fee is $99 USD including taxes.

AI-900 Course Outline

Now, the candidate should get an idea about the course structure. Below, we are mentioning the course outline that the candidate should know in order to pass the Al-900 exam.

1. Describe Artificial Intelligence workloads and considerations (15-20%)
2. Describe fundamental principles of machine learning on Azure (30-35%)
3. Describe features of computer vision workloads on Azure (15-20%)
4. Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
5. Describe features of conversational AI workloads on Azure (15-20%)