AI-900 Azure AI Fundamentals Complete Exam Prep

Learn the basics of artificial intelligence, its traditional and generative types, and how AI, ML, DL, and NLP drive business use cases in healthcare and finance, with Azure workloads.

Understand deep learning as a subset of machine learning that uses neural networks with multiple layers to learn from large data, using weights and biases to extract hierarchical features.

Explore Azure AI services, a suite of ready-to-use tools and APIs for translation, text analysis, content moderation, and vision tasks, with optional custom models and enterprise-grade security.

Understand how computer vision lets AI see and describe visual world, from grayscale and RGB images to object recognition, convolution and feature maps, with natural language processing, powered by CNNs.

Learn how transformers create vector embeddings to fuse natural language processing with computer vision, enabling image captioning and multimodal models like Florence to learn shared text and image representations.

Explore Azure AI Vision Studio, a comprehensive computer vision suite with pre-built and custom models for image analysis, OCR, object and face detection, and segmentation, with Python and Java support.

Explore Azure AI Vision in Vision Studio to analyze images with object detection, captions and dense captions, image retrieval, OCR, and custom image classification, using SDK or REST APIs.

Learn to implement Azure vision services with rest APIs or the client SDK. Set up a computer vision resource, sign in, configure subscription and endpoint, and run quick start examples.

Explore optical character recognition in Azure Vision Studio, extracting and copying text from images, with character-level JSON position data and support for diverse alphabets and PDFs.

Explore Azure AI Face capabilities, including face detection and analysis (head pose, age), and face recognition (verification, identification, find similar, liveness), with authorization via intake form and responsible AI controls.

Explore hands-on facial analysis with Azure AI Face, including resource setup, image uploads, and face detection with attributes, landmarks, and output in JSON, plus handling limitations like visibility and size.

Master the fundamentals of machine learning, including data preparation, algorithms, and training a model. Explore how training, validation, and test sets assess performance on unseen data and model fit.

Explore the three main types of machine learning: supervised, unsupervised, and reinforcement learning, with labeled data, classification and regression tasks, and practical examples.

Explore classification in supervised learning, defining true positives, false positives, and the confusion matrix, and using ROC and thresholds to evaluate binary and multi-class models.

Explore regression models as a supervised learning approach to predict continuous values from input features and minimize errors using metrics like mean absolute error, mean squared error, and r squared.

Explore how clustering groups unlabeled data into cohesive, distinct clusters using similarity, with metrics like intra cluster distance, inter cluster distance, maximum distance, and silhouette score.

Set up a full machine learning workflow in Azure Machine Learning Studio to predict car prices by building, training, and deploying a model within a workspace for production.

Set up a compute instance or scalable cluster in azure machine learning studio, and use the designer to train and deploy a regression model on automobile price data.

Clean and transform data in Azure ML by selecting price-related top features, applying min-max normalization, handling missing values, and preparing for training and evaluation.

Learn to train and evaluate a model in azure machine learning by cleaning and normalizing data, splitting into train and test sets, and evaluating a regression model.

Create and deploy a real-time inference endpoint in Azure Machine Learning, wiring a web service input to a scoring model to test predictions and monitor endpoint health.

Clean up your resources after completing tasks by deleting endpoints (container instances), models, pipelines, the workspace, and the resource group to avoid costs, confirming deletions by typing the resource name.

Explore how large language models use tokens and vector embeddings to understand semantics and generate human-like text for tasks like answering questions and summarizing text.

Learn the essentials of prompt engineering for Azure AI fundamentals, defining prompts as inputs to large language models and the four elements—instructions, context, input data, output indicator—plus negative prompting.

Explore prompt engineering techniques to improve model responses, including zero-shot, few-shot, and chain-of-thought prompting, with practical examples illustrating how to structure prompts and reasoning steps.

Explore how Azure AI Foundry unifies model catalog, customization tools, and scalable deployment to build generative AI solutions, with pre-trained models, fine-tuning, and responsible AI features.

Explore Azure AI Foundry, integrated Azure platform for selecting, deploying, and fine-tuning models like GPT four and Llama, set up a project and endpoints to power chatbots and support bots.

Explore Azure AI Foundry by navigating the model catalog, create and customize a project, deploy and fine-tune models, apply safety measures, use the playground to test and iterate before cleanup.

Learn how to fine-tune and customize an azure openai model with system prompts and prompt flow, deploy it to an endpoint, and test it in the playground.

Explore the Azure OpenAI playground, deploy a base model, and enhance prompts with safety messages, examples, and variables while managing chat history, data indexing, and endpoint integration.

Learn to clean up Azure AI Foundry resources by deleting the deployment, the instance, the storage account, and the resource group to stay organized.

Explore Microsoft Copilot, an AI-powered assistant integrated into Word, Excel, PowerPoint, Teams, and Outlook, that drafts content, summarizes conversations, analyzes data, and automates repetitive tasks.

Discover azure ai language in the language studio, featuring sentiment analysis, key phrase extraction, named entity recognition with pii detection, and tools for conversational understanding and question answering.

Explore Azure Language Studio to create a custom question answering knowledge base from documents, deploy and test it, and integrate with GPT-4 using the prediction URL.

Explore how conversational language understanding maps user utterances to intents using entities, training data, and Azure AI services to improve chatbots, voice assistants, and enterprise apps.

Explore Azure AI speech's suite—speech to text, text to speech with neural voices, speech translation, speaker recognition, and pronunciation assessment—for real-time or batch voice apps.

Learn to integrate Azure AI translator for asynchronous batch and synchronous document translations, preserving layout, using blob storage, and applying custom translation models, real-time translation, and broad language support.

Explore conversational language understanding in Azure Language Studio, training a hotel booking model with intents and entities, then deploy and test predictions via a deployment endpoint.

Explore the speech studio's text-to-speech and speech-to-text features, including real-time captioning, video translation, and a language learning assistant, with limited access for personal voice.

Discover how document intelligence uses AI and OCR to automatically extract data from scanned documents and forms, including key value pairs and tables, to reduce manual processing and errors.

Use Azure AI Document Intelligence Studio to extract and classify data from documents with prebuilt and custom models, then configure resources and run analyses on invoices and other documents.

Discover knowledge mining with Azure AI search to extract insights from unstructured data, transform it into structured, searchable indexes, and apply OCR, entity recognition, and translation with semantic search.

Explore responsible ai principles to ensure trustworthy, transparent systems across lifecycle for both traditional and generative ai. Identify bias, variance, underfitting, and overfitting challenges and strategies to balance performance.

Apply cross-validation to improve generalization and mitigate bias and variance. Leverage regularization, dimension reduction, simpler models, and early stopping to reduce overfitting and build robust AI models.

Explore the dimensions of responsible AI, including fairness, explainability, privacy and security, transparency, governance, safety, and controllability, and understand how they support non-discrimination, understandable decisions, and alignment with human values.

Explore model trade-offs in responsible AI, balancing interpretability and performance, safety and transparency, controllability and complexity, and bias and variance to design explainable AI with human-centered principles.

Apply human centered design principles for explainable AI to support amplified and unbiased decision making, plus human and AI learning through reinforcement learning from human feedback and SageMaker Ground Truth.

Use practice tests and Microsoft sample questions to strengthen knowledge before the AI-900, and understand booking steps, check-in rules, and exam logistics.

Learn how to claim your badge after the exam, accept it via the Microsoft Cradley flow, and share or download the badge to add to LinkedIn, your CV, or profile.