AI-900 is an exam offered by Microsoft that focuses on the fundamentals of artificial intelligence (AI) and Microsoft Azure AI services. The exam measures the ability of individuals to understand and use AI services, including language processing, computer vision, speech recognition, and decision-making. It is intended for individuals who are new to AI and interested in developing AI solutions on the Microsoft Azure platform.
The exam is designed to test the candidate's knowledge and skills in implementing Azure AI services such as Azure Cognitive Services, Azure Bot Service, and Azure Databricks, and it is up to Microsoft's discretion to determine what score is considered a passing score. It is recommended to prepare well and refer to the official Microsoft documentation and resources to increase the chances of passing the exam.
The exam is designed to test the candidate's knowledge and skills in implementing Azure AI services such as Azure Cognitive Services, Azure Bot Service, and Azure Databricks, and it is up to Microsoft's discretion to determine what score is considered a passing score. It is recommended to prepare well and refer to the official Microsoft documentation and resources to increase the chances of passing the exam.
Prerequisites of AI-900
The prerequisites for the AI-900 exam are not specified by Microsoft. However, to take the AI-900: Microsoft Azure AI Fundamentals certification exam, it is recommended that the candidate has a basic understanding of AI concepts, machine learning and data science, and have an understanding of cloud computing and Microsoft Azure services. It is also suggested that the candidate have some experience working with data and developing solutions using Microsoft Azure.
Skills
Measured
The AI-900 exam is intended to measure the candidate's understanding of the basics of Azure Artificial Intelligence (AI) services. The exam is intended to assess the skills of an individual who is starting to work with AI services and is looking to prove their understanding of AI concepts, Azure Cognitive Services, Azure Bot Services, and the AI service offerings within Azure. The specific skills that are measured in the AI-900 exam are:
- Understanding Azure AI Services
- Knowledge of Azure Cognitive Services
- Understanding of Azure Bot Services
- Understanding of Azure AI service offerings
- Ability to implement solutions using Azure AI services
- Ability to secure and manage AI services on Azure.
Describe Artificial Intelligence workloads and considerations (15-20%)
Identify features of common AI workloads
- prediction/forecasting workloads
- features of anomaly detection workloads
- computer vision workloads
- natural language processing or knowledge mining workloads
- 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
Identify common machine learning types
- regression machine learning scenarios
- classification machine learning scenarios
- clustering machine learning scenarios Describe core machine learning concepts
- 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
- core tasks in creating a machine-learning solution
- describe common features of data ingestion and preparation
- describe common features of feature selection and engineering
- describe common features of model training and evaluation
- describe common features of model deployment and management
- describe the capabilities of no-code machine learning with Azure Machine Learning
- Automated Machine Learning tool
- Azure Machine Learning designer
Identify common types of computer vision solutions:
- features of image classification solutions
- features of object detection solutions
- features of semantic segmentation solutions
- features of optical character recognition solutions
- features of facial detection, recognition, and analysis solutions
- Azure tools and services for computer vision tasks
- capabilities of the Computer Vision service
- capabilities of the Custom Vision service
- capabilities of the Face service
- capabilities of the Form Recognizer service
Identify features of common NLP Workload Scenarios
- features and uses for keyphrase extraction
- features and uses for entity recognition
- features and uses for sentiment analysis
- features and uses for language modeling
- features and uses for speech recognition and synthesis
- features and uses for translation
- Azure tools and services for NLP workloads
- capabilities of the Text Analytics service
- capabilities of the Language Understanding Intelligence Service (LUIS)
- capabilities of the Speech service
- capabilities of the Text Translator service
Identify common use cases for conversational AI
- features and uses for webchat bots
- features and uses for telephone voice menus
- features and uses for personal digital assistants
- Azure services for conversational AI
- capabilities of the QnA Maker service
- capabilities of the Bot Framework
Reference - Microsoft Learning path
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