Practice Exams - Microsoft Azure AI-900 - Azure AI Fundament

Practice Exams | Microsoft Azure AI-900 | Azure AI Fundament, available at $44.99, has an average rating of 4.64, 6 quizzes, based on 55 reviews, and has 303 subscribers.

You will learn about Prepare for the Microsoft Azure AI-900 Exam (Artificial Intelligence and Machine Learning) Describe Artificial Intelligence workloads and considerations Identify features of common AI workloads Identify guiding principles for responsible AI Describe fundamental principles of machine learning on Azure Identify common machine learning types Describe core machine learning concepts Identify core tasks in creating a machine learning solution Describe capabilities of no-code machine learning with Azure Machine Learning studio Describe features of computer vision workloads on Azure Identify common types of computer vision solution Identify Azure tools and services for computer vision tasks Describe features of Natural Language Processing (NLP) workloads on Azure Identify features of common NLP Workload Scenarios Identify Azure tools and services for NLP workloads Describe features of conversational AI workloads on Azure Identify common use cases for conversational AI Identify Azure services for conversational AI This course is ideal for individuals who are Beginner database users who want to be Microsoft Azure AI-900 certified. It is particularly useful for Beginner database users who want to be Microsoft Azure AI-900 certified.

Enroll now: Practice Exams | Microsoft Azure AI-900 | Azure AI Fundament

Summary

Title: Practice Exams | Microsoft Azure AI-900 | Azure AI Fundament

Price: $44.99

Average Rating: 4.64

Number of Quizzes: 6

Number of Published Quizzes: 6

Number of Curriculum Items: 6

Number of Published Curriculum Objects: 6

Number of Practice Tests: 6

Number of Published Practice Tests: 6

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Prepare for the Microsoft Azure AI-900 Exam (Artificial Intelligence and Machine Learning)

Describe Artificial Intelligence workloads and considerations

Identify features of common AI workloads

Identify guiding principles for responsible AI

Describe fundamental principles of machine learning on Azure

Identify common machine learning types

Describe core machine learning concepts

Identify core tasks in creating a machine learning solution

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

Describe features of computer vision workloads on Azure

Identify common types of computer vision solution

Identify Azure tools and services for computer vision tasks

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

Identify features of common NLP Workload Scenarios

Identify Azure tools and services for NLP workloads

Describe features of conversational AI workloads on Azure

Identify common use cases for conversational AI

Identify Azure services for conversational AI

Who Should Attend

Beginner database users who want to be Microsoft Azure AI-900 certified.

Target Audiences

Beginner database users who want to be Microsoft Azure AI-900 certified.

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.

NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.

Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.

This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:

Basic cloud concepts

Client-server applications

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

Skills at a glance

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

Describe fundamental principles of machine learning on Azure (20–25%)

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 generative AI workloads on Azure (15–20%)

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

Identify features of common AI workloads

Identify features of content moderation and personalization workloads

Identify computer vision workloads

Identify natural language processing workloads

Identify knowledge mining workloads

Identify document intelligence workloads

Identify features of generative 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 (20–25%)

Identify common machine learning techniques

Identify regression machine learning scenarios

Identify classification machine learning scenarios

Identify clustering machine learning scenarios

Identify features of deep learning techniques

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 Azure Machine Learning capabilities

Describe capabilities of Automated machine learning

Describe data and compute services for data science and machine learning

Describe model management and deployment capabilities in Azure Machine Learning

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 and facial analysis solutions

Identify Azure tools and services for computer vision tasks

Describe capabilities of the Azure AI Vision service

Describe capabilities of the Azure AI Face detection service

Describe capabilities of the Azure AI Video Indexer 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

Describe capabilities of the Azure AI Language service

Describe capabilities of the Azure AI Speech service

Describe capabilities of the Azure AI Translator service

Describe features of generative AI workloads on Azure (15–20%)

Identify features of generative AI solutions

Identify features of generative AI models

Identify common scenarios for generative AI

Identify responsible AI considerations for generative AI

Identify capabilities of Azure OpenAI Service

Describe natural language generation capabilities of Azure OpenAI Service

Describe code generation capabilities of Azure OpenAI Service

Describe image generation capabilities of Azure OpenAI Service

Course Curriculum

Instructors

Wade Henderson
Sr IT Project Manager

Rating Distribution

1 stars: 4 votes

2 stars: 0 votes

3 stars: 5 votes

4 stars: 11 votes

5 stars: 35 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!