Exam Format

The Microsoft Azure AI Fundamentals (AI-900) examination includes between 40 and 60 questions. The following are examples of the different types of questions that could be on the Microsoft Azure AI-900 exam:

Scenario-based single answer questions
Multiple-choice questions
Arrange in the correct sequence type questions
Drag and drop questions
Mark review, drag, and drop, etc.

However, to pass the exam, a candidate needs to earn a score that is at least 700 points high.

Modules and Weightage in the Exam

Modules emphasized in the AI-900 exam are listed in Table 1-1.

Table 1-1AI-900 modules and their weightage

Module Name	Weightage
Describe artificial intelligence workloads and considerations	20–25%
Describe fundamental principles of machine learning on Azure	25–30%
Describe features of computer vision workloads on Azure	15–20%
Describe features of natural language processing (NLP) workloads on Azure	25–30%

Module Description

In this section, you’ll look at each module closely and try to figure out how it fits into the whole experience.

Module 1: Describe Artificial Intelligence Workloads and Consideration (20–25%)

Module 1 talks about the fundamentals of artificial intelligence as well as artificial intelligence in Azure.

This module teaches you two lessons.

Lesson 1: Identify Features of Common AI Workloads

Identify features of anomaly detection workloads
Identify computer vision workloads
Identify natural language processing workloads
Identify knowledge mining workloads

Lesson 2: Identify Guiding Principles of 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

Module 2: Describe Fundamental Principles of Machine Learning on Azure (25–30%)

The module introduces machine learning as well as machine learning in Azure.

This module teaches you three lessons.

Lesson 1: Identify Common Machine Learning Types

Identify regression machine learning scenarios
Identify classification machine learning scenarios
Identify clustering machine learning scenarios

Lesson 2: 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

Lesson 3: Describe Capabilities of Visual Tools in Azure Machine Learning Studio

Automated machine learning
Azure Machine Learning Designer