Azure AI-900 study guide
Azure AI-900 · Work through your exam, one topic at a time.
Exam AI-900: Microsoft Azure AI Fundamentals is Microsoft's entry-level AI credential exam, aimed at candidates at the beginning of a career in AI solution development. Microsoft describes the audience as people with conceptual knowledge of AI solutions in Azure plus foundational technical skills, Python coding familiarity, and comfort with Azure resources; data science and software engineering experience are not required, though basic cloud and client-server awareness helps. The Azure AI-900 study guide has 5 sections and about 1,747 words.
01Describe Artificial Intelligence Workloads and Considerations
AI-900 expects you to match a scenario to a workload family : machine learning (predicting a value or category from data), computer vision (interpreting images and video), natural language processing (understanding, translating and producing text and speech), document intelligence (pulling structured fields out of forms, invoices and receipts), knowledge mining (building a searchable index over unstructured content), and generative AI (creating new text, images or code). Fairness - similar outcomes for people in similar situations, regardless of group.
349 words, about 2 min02Describe Fundamental Principles of Machine Learning on Azure
Supervised learning trains on labelled data - each row has features (inputs) and a label (the answer). Two flavours: regression predicts a numeric value (price, units, temperature); classification predicts a category (yes/no, or one of several classes).
323 words, about 2 min03Describe Features of Computer Vision Workloads on Azure
Image classification - one or more labels describing the whole image ("dog"). Object detection - a class label plus a bounding box for each instance, and more than one object can be returned.
356 words, about 2 min04Describe Features of Natural Language Processing (NLP) Workloads on Azure
Language detection - which language is this text? Sentiment analysis and opinion mining - positive/neutral/negative, and at aspect level ("the food was great but the service was slow").
367 words, about 2 min05Describe Features of Generative AI Workloads on Azure
Large language models (LLMs) are trained on huge text corpora to predict the next token . At inference you send a prompt and receive a completion .
352 words, about 2 min