Are you still not ready to take the Microsoft AI-900 Azure AI Fundamentals test? Don’t worry! Let’s get to the bottom of everything about the exam. Our Ultimate Cheat Sheet will provide all the necessary information and resources to help you prepare for the exam. This Cheat Sheet will help you pass the exam with flying colors.
Microsoft AI-900 – Overview
The Microsoft Azure AI Fundamentals (AI900) certification exam is for candidates with basic and foundational knowledge of machine learning (ML), AI concepts, and related Microsoft Azure services. Candidates will also be able to demonstrate their knowledge about common ML and AI workloads, and how they can be implemented on Azure.
Target Audience
Candidates with technical and non-technical backgrounds are encouraged to take the Microsoft Azure AI-900 certification exam.
Microsoft AI-900
Professional certifications can make you more job-ready and give you a competitive edge. Preparing to take a certification exam is like planning to achieve any important goal. This Microsoft Azure AI-900 exam cheatsheet provided a quick overview of the exam and its resources. It is easy to understand and can be very helpful for last-minute revisions.
Check out the AI-900 Exam Topics
Start by visiting Microsoft’s official website. This is a smart move as you want to only trust the best website for authentic information. All information about the Microsoft Azure AI900 exam can be found on the portal. The portal covers everything you need to know about the exam, including the pattern and all the modules and study materials. You should be familiar with the objectives and course domains for the exam. Spend enough time on each topic and be familiar with the subject. This will help you prepare better.
Microsoft AI-900 Exam: Updates to the course outline as of January 27, 2021
The latest Microsoft AI-900 exam topics are:
Topic 1: Describe Artificial Intelligence Workloads and Considerations (15-20%)
1.1 First, identify common AI workloads
identifying the prediction/forecasting workloads (Microsoft Documentation:Demand Forecasting)
You can also identify the characteristics of anomaly detection workloads
identify computer vision workloads (Microsoft Documentation:Applying content tags to images,Detect common objects in images,Detect popular brands in images)
Moreover, identifying natural language processing or knowledge mining workloads (Microsoft Documentation:Choosing a natural language processing technology in Azure)
identifying conversational AI workloads (Microsoft Documentation:Microsoft Conversational AI tools)
1.2 Second, identify the guiding principles for responsible AI
Microsoft Documentation:Identify guiding principles for responsible AI
The considerations for fairness in an AI solution
Explaining the safety and reliability of an AI solution is also important.
A description of the privacy and security considerations in an AI solution
Furthermore, we will discuss the considerations of inclusion in an AI solution
This article explains the importance of transparency in an AI solution
This article explains the importance of accountability in an AI solution
Topic 2: Describe the fundamental principles of machine-learning on Azure (30-35%)
2.1 Identify common types of machine learning
identifying regression machine learning scenarios (Microsoft Documentation:Linear Regression)
identifying classification machine learning scenarios (Microsoft Documentation:Classification modules)
identify clustering machine learning scenarios (Microsoft Documentation:Clustering modules)
2.2 Next, describe core machine learning concepts
Machine learning can be done by identifying features and labels within a dataset
Describe how machine learning uses validation and training datasets
This article describes how machine learning algorithms can be used to train models
selecting and interpreting model evaluation metrics for classification and regression (Microsoft Documentation:Metrics for classification and regression models)
2.3 Identify the core tasks for creating a machine-learning solution
Common features of data preparation and ingestion (Microsoft Docation:Data ingestion options to Azure Machine Learning workflows).
Describe common features of feature selection, engineering and engineering
describing common features of model training and evaluation (Microsoft Documentation:Evaluate Model)
Explain the most common features of model management and deployment (Microsoft Doctation:Deploy real time machine learning services with Azure Machine Learning. MLOps: Model management and deployment with Azure Machine Learning.
2.4 Additional, describe the capabilities of Azure Machine Learning’s no-code machine intelligence:
automated ML Wizard UI (Microsoft Documentation:Automated machine learning (AutoML))
Azure Machine Learning designer (Microsoft Documentation:Azure Machine Learning designer)
Topic 3: Describe the features of computer vision workloads in Azure (15-20%)
3.1 First, identify common types of computer-vision solution:
Identifying features of image classification solutions (Microsoft Docation:Train image classification model with MNIST data, scikit-learn).
Also, identify the features of object detection solutions (Microsoft Docation: Detect common objects in images).
Identifying features of semantic segmentation solutions (Microsoft documentation:
Further, identifying features of optical character recognition solutions (Microsoft Documentation:Optical Character Recognition (OCR))
Identify facial recognition and recognition features (Microsoft Documentation – Face detection and attributes, Face recognition concepts)
3.2. Second, Identify Azure tools for computer vision tasks
Identify the capabilities of the Computer Vision Ser