Microsoft Azure (AI-900) Fundamentals Practice Exam : 2024

The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them.

This exam measures your ability to accomplish the following technical tasks: manage Azure identities and governance; implement and manage storage; deploy and manage Azure compute resources; configure and manage virtual networking; and monitor and back up Azure resources.

You won’t be hoping you are ready, you will know you are ready to sit for and pass the exam. After practicing these tests and scoring an 80% or higher on them, you will be ready to PASS on the first attempt and avoid costly re-schedule fees, saving you time and money.

This Practice course gain access to expert instruction, hands-on practice, and insights into AI in Microsoft Azure. It opens doors to the powerful world of AI with Azure’s comprehensive suite of services. Beyond technical skills, this course will guide you on how to implement AI solutions ethically and responsibly, ensuring that your AI initiatives align with industry best practices

AI-900 : Microsoft Azure AI Fundamentals Exam details :

• Exam Name: Microsoft Certified – Azure AI Fundamentals

• Exam Code: AI-900

• Exam Price:$99 (USD)

• Number of Questions: Maximum of 40-60 questions,

• Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,

• Length of Test: 60 Minutes. The exam is available in English and Japanese languages.

• Passing Score: 700 / 1000

• Languages : English, Japanese, Korean, and Simplified Chinese

• Schedule Exam : Pearson VUE

AI-900 : Microsoft Azure AI Fundamentals Certification Exams skill questions:

Skill Measurement Exam Topics:–

• 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%)

#) 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 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 Power Virtual Agents and the Azure Bot service

Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; and features of Natural Language Processing (NLP) workloads on Azure.

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