Exam Objectives
This exam is designed for candidates looking to demonstrate foundational knowledge on the considerations and benefits of adopting cloud services in general and the Software as a Service (SaaS) cloud model. This exam will also cover knowledge of available options and benefits gained by implementing Microsoft 365 cloud service offerings.
This exam can be taken as a precursor to cloud computing and technologies exams such as Office 365, Microsoft Intune, Azure Information Protection (AIP), and Windows 10.
You can use Azure AI Fundamentals to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.
Skills at a glance
- Describe Artificial Intelligence workloads and considerations (15–20%)
- Describe fundamental principles of machine learning on Azure (15–20%)
- Describe features of computer vision workloads on Azure (15–20%)
- Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
- Describe features of generative AI workloads on Azure (20–25%)
Describe Artificial Intelligence workloads and considerations (15–20%)
Identify features of common AI workloads
- Identify computer vision workloads
- Identify natural language processing workloads
- Identify document processing workloads
- Identify features of generative 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 (15-20%)
Identify common machine learning techniques
- Identify regression machine learning scenarios
- Identify classification machine learning scenarios
- Identify clustering machine learning scenarios
- Identify features of deep learning techniques
- Identify features of the Transformer architecture
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
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
- Describe capabilities of the Azure AI Vision service
- Describe capabilities of the Azure AI Face detection 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
- Describe capabilities of the Azure AI Language service
- Describe capabilities of the Azure AI Speech service
Describe features of generative AI workloads on Azure (20–25%)
Identify features of generative AI solutions
- Identify features of generative AI models
- Identify common scenarios for generative AI
- Identify responsible AI considerations for generative AI
Identify generative AI services and capabilities in Microsoft Azure
- Describe features and capabilities of Azure AI Foundry
- Describe features and capabilities of Azure OpenAI service
- Describe features and capabilities of Azure AI Foundry model catalog
MCF Overview
The pathway to AI, Data Science, and Cloud Computing.
The Microsoft Certified Fundamentals exams are the pathway to fully understanding what cloud computing is, what options are available from Microsoft, and which options make sense for businesses to invest in. This set of exams is for students who are exploring a career in artificial intelligence, data science, and cloud computing. With the usage of big data and artificial intelligence on the rise, the demand for data scientists is not being met—and the gap is widening.
MCF Certifications
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