Microsoft Certified: Azure AI Fundamentals AI-900 [2026]

Explore how artificial intelligence uses data and algorithms to learn, recognize patterns, understand language, and make decisions. AI analyzes data to predict outcomes and assist diagnoses.

Explore common AI use cases across industries—computer vision, natural language processing, conversational AI, machine learning, anomaly detection, speech AI, and recommendation systems, with Azure service examples.

AI powers everyday apps to boost convenience, efficiency, and decision-making across devices, search, social media, e-commerce, streaming, email, smart homes, healthcare, and finance.

Explore Azure AI services, including computer vision, language, speech, and conversational AI, plus Azure Machine Learning, Azure OpenAI service, and responsible AI principles.

Explore how machine learning, a subset of artificial intelligence, learns from data to predict outcomes, with supervised, unsupervised, and reinforcement learning, and tools like azure machine learning and azure automl.

Explore supervised and unsupervised machine learning, including regression and classification. Learn binary and multi-class classification, and how clustering groups by features.

Identify features as input variables used to train predictions and labels as outputs the model predicts, with examples like age, salary, house price, and cat or dog.

Learn how to split data into training, validation, and testing sets to train, tune, and evaluate a model, prevent overfitting, and measure unbiased performance via cross-validation in Azure Machine Learning.

Use machine learning for data patterns to enable predictions, classifications, including anomaly detection, image and speech recognition. Avoid machine learning for rules or when explainability and 100% accuracy are required.

Explore responsible AI as the practice of designing and using ethical, trustworthy, and safe AI systems. Learn how fairness, transparency, privacy, security, reliability, and accountability guide Azure AI 900 principles.

Explore fairness in AI systems, preventing bias and discrimination through diverse datasets, bias detection, and monitoring, supported by Azure machine learning fairness assessment tools for responsible AI.

Develop reliable and safe AI that performs consistently under expected and unexpected data and edge cases, with extensive testing, monitoring, fallbacks, human oversight, and controlled Azure deployments and rollback options.

Protect personal data and ensure security in AI by enforcing encryption, access controls, and threat monitoring, while aligning with Azure AD and compliance standards.

Discover how inclusive AI solutions benefit everyone by designing for diverse abilities, languages, and devices, with examples of accents, accessibility, multilingual chatbots, and global Azure AI services.

Explain how and why AI makes decisions to users, stakeholders, and regulators, using explainable AI techniques and Azure interpretability tools to foster trust, accountability, and bias detection.

Accountability in AI means humans are responsible for outcomes, with ownership, oversight, and control over AI design, deployment, and decisions, using audit logs and governance and compliance tools in Azure.

Discover Azure machine learning concepts and the Azure ML workspace, covering datasets, compute resources, experiments, models, training, AutoML, deployment, inference, and monitoring.

Explore end-to-end machine learning workflows in Azure, from data ingestion and preparation to training, evaluation, registration, deployment, inference, and continuous monitoring.

Train and register models in Azure Machine Learning and manage versions. Run experiments to compare metrics and deploy endpoints for real-time or batch inference.

Learn about low-code ML options in Azure, including AutoML, Azure ML Designer, and Azure AI services for vision, OCR, speech, language, and decision services.

Explore how computer vision enables machines to interpret images and videos by identifying objects, recognizing faces, reading text, and analyzing scenes, using Azure AI Vision and pre-built vision services.

Explore how image classification assigns labels to an entire image by learning visual patterns from labeled training data, and use Azure AI Vision for pre-built models.

Explore face detection concepts and how AI systems identify the presence and location of human faces in images or videos, including bounding boxes, attributes, and landmarks.

Learn how object detection identifies objects in images and locates them with bounding boxes, labels, and confidence scores, and how Azure AI Vision supports detecting multiple objects with location data.

Explore optical character recognition (OCR) and how it extracts printed or handwritten text from images and documents, converting it into machine-readable text with Azure AI Vision OCR Read API.

Introduce natural language processing, enabling computers to understand and generate human language in text and speech. Explore nlp workloads like sentiment analysis, translation, and question answering with Azure AI services.

Explore text analysis and sentiment detection in Azure AI language to extract meaning, identify entities and topics, and detect emotion with labels like positive, negative, natural, and mixed.

Key phrase extraction uses Azure AI language text analytics to identify important words and phrases, summarize meaning, and highlight main topics for tagging and indexing.

Understand language understanding concepts that enable ai to identify user intent and extract entities from utterances, enabling contextually smart responses in chatbots, assistants, and voice apps.

Discover how azure language service delivers pre-built nlp to analyze text and conversation. Explore features like text analysis, sentiment, key phrases, ner, language detection, intents, and q&a, with no code.

Speech-to-text converts spoken audio into written text, enabling real-time and batch transcription across accents and languages, with Azure AI Speech powering applications like voice assistants and captions.

Explore text-to-speech concepts, including converting written text into natural sounding audio, supporting multiple languages and voices, and Azure AI Speech real-time and batch synthesis for accessibility and user communication.

Learn how speech translation combines speech recognition, translation, and optional text-to-speech to convert spoken language in real time or from recordings, with Azure AI Speech.

Discover how Azure Speech Service combines speech-to-text, speech recognition, text-to-speech, translation, and speaker recognition with pre-trained models, real-time and batch processing, and privacy considerations.

Explore how generative AI creates new content from data patterns, including text, images, audio, video, and code, and how Azure OpenAI enables these capabilities with responsible AI.

Explore large language models, generative AI that understand and generate human language from massive text data. Discover transformer architectures and Azure OpenAI Service enabling translation, summarization, code explanations, with security.

Explore generative AI use cases such as text generation, conversational AI, content summarization, translation, code and image generation, and data augmentation within Azure OpenAI service while applying responsible AI practices.

Explore how Azure OpenAI Service provides enterprise-grade access to generative AI models and LLMs with security, privacy, and compliance, enabling chatbots, content generation, and co-pilots.