AI-900: Microsoft Azure AI Fundamentals Practice Exam Jan 23

Complete preparation & practice tests for the AI-900 Microsoft Azure AI Fundamentals.

Artificial intelligence and machine learning are all set to dictate the future of technology. The focus of Microsoft Azure on machine-learning innovation is one of the prominent reasons for the rising popularity of Azure AI. Therefore, many aspiring candidates are looking for credible approaches for the AI-900 exam preparation that is a viable instrument for candidates to start their careers in Azure AI.

The interesting fact about the AI-900 certification is that it is a fundamental-level certification exam. Therefore, candidates from technical as well as ones with non-technical backgrounds can pursue the AI-900 certification exam. In addition, there is no requirement for software engineering or data science experience for the AI-900 certification exam.

The AI-900 certification can also help you build the foundation for Azure AI Engineer Associate or Azure Data Scientist Associate certifications.

Skills measured

• 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%)

Topics covered on each functional group in exam

Describe Artificial Intelligence workloads and considerations (20— 25%)

• 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

• 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 (25— 30%)

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 capabilities of visual tools in Azure Machine Learning Studio

• Automated machine learning

• 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 (25—30%)

• 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 Language service

• Identify capabilities of the Speech service

• Identify capabilities of the Translator service

Identify considerations for conversational AI solutions on Azure

• Identify features and uses for bots

• Identify capabilities of the Azure Bot service

Who this course is for:

• Candidates who are interested to learn Machine Learning
• Candidates with technical backgrounds who are interested in getting Microsoft Azure AI Certification