Microsoft Azure AI Fundamentals Exam Syllabus

Azure AI Fundamentals PDF, AI-900 Dumps, AI-900 PDF, Azure AI Fundamentals VCE, AI-900 Questions PDF, Microsoft AI-900 VCE, Microsoft Azure AI Fundamentals Dumps, Microsoft Azure AI Fundamentals PDFUse this quick start guide to collect all the information about Microsoft Azure AI Fundamentals (AI-900) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the AI-900 Microsoft Azure AI Fundamentals exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft Azure AI Fundamentals certification exam.

The Microsoft Azure AI Fundamentals certification is mainly targeted to those candidates who want to build their career in Microsoft Azure domain. The Microsoft Certified - Azure AI Fundamentals exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft Azure AI Fundamentals.

Microsoft Azure AI Fundamentals Exam Summary:

Exam Name Microsoft Certified - Azure AI Fundamentals
Exam Code AI-900
Exam Price $99 (USD)
Duration 60 mins
Number of Questions 40-60
Passing Score 700 / 1000
Books / Training Course AI-900T00: Microsoft Azure AI Fundamentals
Schedule Exam Pearson VUE
Sample Questions Microsoft Azure AI Fundamentals Sample Questions
Practice Exam Microsoft AI-900 Certification Practice Exam

Microsoft AI-900 Exam Syllabus Topics:

Topic Details

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

To ensure success in Microsoft Azure AI Fundamentals certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Microsoft Azure AI Fundamentals (AI-900) exam.

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