Cloud Certifications: Microsoft Certified: Azure AI Fundamentals
Introduction
Cloud-based solutions have been high in demand over the last several years, and this is not likely to change in the future. Today, organizations of all shapes and sizes increasingly use cloud-based software and services.
We have come a long way since the iconic AI that was HAL 9000 in the awesome film 2001: A Space Odyssey from Stanley Kubrick. From digital voice assistants to electric cars, AI is sitting at the core of a huge surge of modern technologies.
In this guide, you will learn about the Microsoft Azure AI Fundamentals certification and the exam you can take to achieve it.
The Microsoft Azure AI Fundamentals certification follows Microsoft's departure from broader certifications like the Microsoft Certified Systems Administrator (MCSA) or its older sibling, the Microsoft Certified Systems Engineer (MCSE). Nowadays the focus is on specific roles.
Target Audience
As a candidate for this exam, you should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services. By taking this exam, you will demonstrate your knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Applicable Exams
A single exam, the AI-900, is required to gain the Microsoft Azure AI Fundamentals certification. It is important to understand that Microsoft has taken up the practice of retiring and replacing exams at a much faster pace than in the past. Since the cloud is ever-changing, Microsoft updates live exams frequently.
The price for the exam is US$99/€99. Microsoft offers a student discount if you verify your academic status when booking the exam by using one of the following: a school email account, a school account, an International Student Identity Card, a verification code, or other documentation proving your eligibility for the student discount.
Prerequisites
While there are no specific prerequisites to taking the AI-900 exam, it is worth noting that experience with the required skills is key to a successful experience.
Ensure that you possess sufficient experience and invest the time to go through the relevant Pluralsight courses and other resources.
Skills Measured
Your skills will be measured in the following five categories:
- Describe AI Workloads and Considerations (15-20%)
- Describe Fundamental Principles of Machine Learning on Azure (30-35%)
- 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 Conversational AI Workloads on Azure (15-20%)
These categories are broken down into details as follows, according to the exam skills outline:
Describe Artificial Intelligence Workloads and Considerations
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
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 common features of feature selection and engineering
- 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
- Automated Machine Learning tool
- Azure Machine Learning designer
Describe Features of Computer Vision Workloads on Azure
Identify common types of computer vision solution
- Identify features of image classification solutions
- Identify features of object detection solutions
- Identify features of semantic segmentation solutions
- Identify features of optical character recognition solutions
- Identify features of facial detection, recognition, and 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
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 Intelligence Service (LUIS)
- Identify capabilities of the Speech service
- Identify capabilities of the Text Translator service
Describe Features of Conversational AI Workloads on Azure
Identify common use cases for conversational AI
- Identify features and uses for webchat bots
- Identify features and uses for telephone voice menus
- Identify features and uses for personal digital assistants
Identify Azure services for conversational AI
- Identify capabilities of the QnA Maker service
- Identify capabilities of the Bot Framework
Pluralsight Courses
Make sure you check out Pluralsight's Microsoft Azure AI Engineer (AI-100) learning path, which currently contains several suitable courses for this certification.
Other Resources
Microsoft Learn provides training resources free of charge. Take a look at the following learning path:
Utilizing the Microsoft Docs and navigating to the relevant topics will also enable you to prepare for this exam.
Compensation and Employment Outlook
The cloud business has been booming in the last several years. Microsoft is a leader in this area and keeps growing. While COVID-19 has affected everyone in some way, it certainly doesn't seem to have had a negative impact on Microsoft's cloud growth.
Gaining an up-to-date certification like the Microsoft Azure AI Fundamentals certification from a household name like Microsoft should make you much more attractive to both current and future employers, especially since the cloud is booming. Your current employer might not raise your salary, but the next time you go looking for a job make sure you check trusted Internet sources for up-to-date information on salaries in your region.
It's difficult to provide absolute figures because they will depend on numerous factors like your experience, company type and size, industry, and region, especially given the wide range of potential job roles in the artificial intelligence space, such as machine learning engineer, data scientist, business intelligence developer, research scientists, and AI engineer. According to PayScale.com, you can expect salaries for artificial intelligence engineers and specialists to average around US$122,127 in the United States.
Conclusion
As a fundamentals-level certification, the Microsoft Azure AI Fundamentals credential, while challenging, will earn you recognition as a subject matter expert in this field. All it takes is a single exam.
Sign up for Azure to utilize the free features and services and book the exam, which you can take right in your home or in one of many testing centers.
I hope that this guide is useful and wish you good luck in gaining your certification.