[Aug 2024] AI-900: AI Fundamentals Exam Prep In One Day

The course is designed as per Skill area as of April 24, 2024. This course completely covers the AI-900 exam from start to finish. Always updated with the latest requirements.

This study guide will help you understand what to expect on the exam. The information in this video course should help you focus your studies as you prepare for the exam.

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

Microsoft Azure is still the fastest-growing large cloud platform. The opportunities for jobs in cloud computing are still out there, and finding well-qualified people is the problem that businesses have.

Earning a Microsoft Certification is global evidence of real-world skills. A Microsoft Certification showcases your commitment to keeping pace with rapidly changing technology and positions you for increased skills, efficiency, and earning potential in your professional roles.

35% of technical professionals say getting certified led to increased income and 26% percent reported promotions.

Exam Details

Exam Name: AI-900: Microsoft Azure AI Fundamentals

Number of Questions in AI 900 Microsoft Certification Exam: 35 to 55

Exam Duration: 60 mins

AI 900 Certification Passing Score: 700 points out from 1000
The number of questions on azure artificial intelligence certification exam is subject to change as Microsoft make updates to ensure it aligns with latest changes in the technology that fits the job role.

AI 900 Certification Exam Fees: $99 USD* Price based on the country or region in which the exam is proctored.

The course is designed as per Skill area as of April 24, 2024. This course completely covers the AI-900 exam from start to finish. Always updated with the latest requirements

AI 900: Microsoft Azure AI Fundamentals Exam Syllabus

Complete exam syllabus is given over here. That is, what are topics that will be covered in below sections.

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

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

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 generative AI workloads on Azure (15–20%)

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

Identify features of common AI workloads

• Identify features of data monitoring and anomaly detection workloads, content moderation and personalization workloads, generative AI workloads.
• Identify computer vision workloads, natural language processing workloads, knowledge mining workloads, document intelligence workloads.

Identify guiding principles for responsible AI

• Describe considerations for fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability in an AI solution.

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

Identify common machine learning techniques

• Identify regression, classification, 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.

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

Identify common types of computer vision solution

• Identify features of image classification, object detection, optical character recognition (OCR), facial detection and facial analysis solutions.

Identify Azure tools and services for computer vision tasks

• Describe capabilities of the Azure AI Vision service, Face detection service, Video Indexer service.

4) 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, entity recognition, sentiment analysis, language modelling, speech recognition and synthesis and identify features and uses for translation.

Identify Azure tools and services for NLP workloads

• Describe capabilities of the Azure AI Language service, Speech service, Translator service.

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

Identify features of generative AI solutions

• Identify features of generative AI models, common scenarios for generative AI and 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.

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