In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.

The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.

There are no prerequisites for this course, however students with some IT knowledge or experience will find the concepts easier to understand.

The Fundamentals certifications validate foundational knowledge of cloud computing services and how those services are provided with Microsoft Azure.

• Each question has detailed explanations at the end of each set that will help you gain a deeper understanding of the MS Azure services.

• The explanation provides an overview of the topic, reference links to Azure docs and a rationale on why the option is correct or incorrect

Skills measured on Microsoft Azure AI-900 Exam

Describe Artificial Intelligence workloads and considerations (15-20%)

Identify features of common AI workloads

• identify prediction/forecasting workloads

• identify features of anomaly detection workloads

• identify computer vision workloads

• identify natural language processing or knowledge mining workloads

• identify conversational AI workloads

Identify guiding principles for 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

Describe fundamental principles of machine learning on Azure (30- 35%)

Identify common machine learning types

• identify regression machine learning scenarios

• identify classification machine learning scenarios

• identify clustering machine learning scenarios

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

• describe how machine learning algorithms are used for model training

• select and interpret model evaluation metrics for classification and regression

Identify core tasks in creating a machine learning solution

• describe common features of data ingestion and preparation

• describe feature engineering and selection

• describe common features of model training and evaluation

• describe common features of model deployment and management

Describe capabilities of no-code machine learning with Azure Machine Learning studio

• automated ML UI

• azure Machine Learning designer

Describe features of computer vision workloads on Azure (15-20%)

Identify common types of computer vision solution

• identify features of image classification solutions

• identify features of object detection solutions

• identify features of optical character recognition solutions

• identify features of facial detection, facial recognition, and facial analysis solutions

Identify Azure tools and services for computer vision tasks

• identify capabilities of the Computer Vision service

• identify capabilities of the Custom Vision service

• identify capabilities of the Face service

• identify capabilities of the Form Recognizer service

Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)

Identify features of common NLP Workload Scenarios

• identify features and uses for key phrase extraction

• identify features and uses for entity recognition

• identify features and uses for sentiment analysis

• identify features and uses for language modeling

• identify features and uses for speech recognition and synthesis

• identify features and uses for translation

Identify Azure tools and services for NLP workloads

• identify capabilities of the Text Analytics service

• identify capabilities of the Language Understanding service (LUIS)

• identify capabilities of the Speech service

• identify capabilities of the Translator Text service

Describe features of conversational AI workloads on Azure (15-20%)

Identify common use cases for conversational AI

• identify features and uses for webchat bots

• identify common characteristics of conversational AI solutions

Identify Azure services for conversational AI

• identify capabilities of the QnA Maker service

• identify capabilities of the Azure Bot service

The exam is available in the following languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish

IMPORTANT: Be aware that the exams will always use product names and terms in English so the learner must be familiar with many terms in English regardless of the language the exam.