AI-901 Exam Prep : Microsoft Azure AI Fundamentals

Azure machine learning is a cloud-based platform that enables developers and data scientists to prepare data, train, experiment, deploy, and monitor models in a collaborative workspace lifecycle.

AutoML automates data preprocessing, algorithm selection, feature engineering, hyperparameter tuning, and model evaluation, enabling fast, accessible model development for prediction, classification, forecasting, and recommendations.

Master regression, a supervised learning method, to predict continuous values from features and labels, with examples like house prices and salaries.

Explore classification in supervised learning, where features lead to labels to categorize data. Assess accuracy and apply to real-world tasks like spam detection, churn prediction, and fraud detection.

Discover hidden groups in data through clustering, an unsupervised technique that uses features to group similar points without predefined labels, enabling customer segmentation and unusual behavior detection.

Explore a data science workspace that unifies compute resources, data management, and model tracking in a single platform, enabling collaboration and efficient machine learning workflows.

Explore model deployment strategies for Microsoft Azure AI Fundamentals by comparing real-time and batch inference, their use cases, benefits, and trade-offs for scalable real-world applications.

Monitor continuously deployed machine learning models to detect model decay and data drift, and apply retraining, updated data, and new features to maintain accurate predictions using Azure AutoML.

Predict retail demand using historical sales and weather data with Azure AutoML to optimize inventory, reduce waste, and improve stock availability.

Explore building multimodal pipelines for Azure OpenAI that process text and image inputs for ai analysis, including base-64 image encoding, interleaved prompts, and vision-capable endpoints, with warehouse quality assurance demo.

Explore how computer vision teaches machines to see and understand images through image classification, object detection, OCR, and image analysis, with real-world uses in security, healthcare, retail, and automation.

Compare image classification and object detection: classification assigns a single label to an image, while detection identifies multiple objects and their locations with bounding boxes.

Explore how optical character recognition reads printed and handwritten text from images, converting it into digital text across languages. See how OCR enables invoice scanning, identity verification, and data-entry automation.

Explore facial detection, analysis, and recognition concepts in AI, showing how systems locate faces, extract attributes and emotions, and identify individuals for security, authentication, and customer insights.

Discover Azure AI Vision Service, a cloud-based, ready-to-use computer vision tool offering image analysis, OCR, object and facial detection, with API integration for app deployment.

Learn how image tagging assigns descriptive keywords and categorization groups images into predefined categories, enabling faster search, better organization, and improved user experience across media, e-commerce, and social platforms.

Learn how image tagging and categorization organize images with keywords and groups, and address challenges while refining these techniques for Custom Vision in niche requirements.

Use custom vision to train your own computer vision models with your data for niche use cases; train with labeled images, evaluate performance, and deploy via APIs with continuous improvement.

Azure Video Indexer extracts insights from video and audio, including speech, text, faces, emotions, objects, and scenes, turning multimedia into searchable, actionable data for media, education, security, and business intelligence.

Automates quality control in manufacturing with Vision AI by training models on labeled product images to detect defects, integrates into the production line for real-time 24x7 inspection with ongoing monitoring.

Learn to build an automated meeting transcription and summarization pipeline with Whisper API v2 in Azure OpenAI, handling ingestion, transcription, and summarization to produce action items and decisions.

Explore how natural language processing enables computers to read, understand, and generate language, with workload scenarios like text analysis, entity recognition, conversational AI, and translation.

Explore keyphrase extraction and entity recognition to identify main ideas and extract names, organizations, and locations, applying these in Azure AI services for business intelligence and feedback analysis.

Learn how sentiment analysis uses natural language processing to classify text by emotional tone as positive, negative, or neutral, with applications in customer feedback and social media.

Explore language modeling and text summarization, two powerful nlp concepts, and see how they enable text generation and concise summaries in chatbots, content generation, document analysis, and business intelligence.

Azure AI Language Service unifies sentiment analysis, keyphrase extraction, entity recognition, and language detection in cloud API. It adds conversational understanding for analyzing customer feedback, support tickets, and employee feedback.

Explore how question answering retrieves accurate answers and how conversational language understanding identifies intent and context, enabling applications in customer support and virtual assistants.

Discover how speech recognition converts audio to text using patterns and language models, and apply this technology to real-world applications like virtual assistants, transcription services, and accessibility tools.

Learn how speech synthesis converts text into natural, human-like speech using neural networks, enabling virtual assistants, navigation, and accessible interfaces through high-quality models and well-formatted input.

Explore real-time translation that uses speech recognition, translation, and speech synthesis to convert speech instantly. Understand challenges like context and idioms, and see Azure speech services enabling global communication.

Build a multilingual customer support hub with NLP, speech recognition, translation, and conversational AI using Azure AI Language, Speech Services, and Translator to serve customers in their own language.

Generative AI creates new content such as text, images, audio, and code by learning patterns from data, moving from analysis to creation and enabling applications in content creation and support.

Understand how large language models read and generate text with tokens, embeddings, and parameters, powering real-world AI tools such as chatbots and content generators in Azure OpenAI service.

Explore the Azure OpenAI Service and its core models—GPT, DALI, and CODEX—and learn to integrate them via APIs to generate text, images, and code in applications while embracing responsible use.

Learn to design clear, context-rich prompts, apply zero-shot, one-shot, and few-shot techniques, and iteratively refine prompts to guide AI models for accurate, useful outputs.

Explore zero-shot, one-shot, and few-shot learning as prompting techniques that guide AI behavior using examples, with practical guidance on when to use each for accuracy and real-world tasks.

Co-pilot, an AI-powered assistant, helps you complete tasks by providing suggestions, generating content, and automating workflows, boosting productivity across writing, emails, coding, summarizing, and analyzing data.

Explore how Copilot, an AI-powered assistant, boosts daily productivity by generating code, drafting documents, and analyzing data, while emphasizing responsible use and context-aware guidance.

Discover how content filtering and safety in generative AI detect and block harmful outputs. Explore built-in Azure OpenAI safeguards and the shared responsibilities to balance creativity and safety.

Discover how Azure OpenAI generates marketing content such as ads, emails, slogans, and social posts by guiding prompts with tone, audience, and goals to speed creativity and personalization.

Explore Microsoft AI Foundry, a unified platform that supports data preparation, model training, deployment, and monitoring, delivering faster, collaborative, production-ready AI solutions across Azure services.

Navigate the model catalog to quickly find and use pre-built AI models, then compare language, vision, and domain models by task, performance, cost, and scalability for integration into applications.

Deploy your first model in AI Foundry by selecting a model, configuring settings, and creating an endpoint, after training, testing, validating, and packaging for production use in real-world applications.

Balance accuracy and grounding to evaluate model performance, ensuring correct outputs are backed by reliable data sources for trustworthy AI in production environments.

Discover how retrieval-augmented generation uses your own data to ground AI responses, integrating real-time retrieval with generation for accurate, domain-specific answers.

Store embeddings in a vector database to enable similarity-based search and fast retrieval. Use cosine similarity or Euclidean distance to find meaning-based matches in semantic search and chatbots.

Explore the AI Foundry SDK: its APIs, libraries, authentication tools, and integration capabilities, and learn how it connects apps to AI services to speed development and enable scalable solutions.

Explore how knowledge-based chatbots solve repetitive questions using retrieval augmented generation and company documents. Learn how data preparation into embeddings, vector databases, and AI Foundry build accurate, fast rack-based chatbots.

Explore how AI agents exceed chatbots by perceiving, reasoning, planning, memory, and tool use to act toward goals.

Explore how AI agents plan actions, execute tasks, and observe outcomes to achieve goals. Understand how planning, execution, observation, and iteration drive autonomous, adaptive behavior.

Explore multi-agent systems where AI agents coordinate, orchestrate, and communicate to achieve goals. Learn use cases in supply chain, healthcare, and smart homes, and the shift toward coordinated AI ecosystems.

Explore tool calling: learn how AI agents invoke external APIs through structured requests to perform real-world tasks and turn AI into action.

Explore how memory and state management give AI agents context, continuity, and personalization by distinguishing short-term and long-term memory and showing practical usage in agentic workflows.

Evaluate AI agents by measuring reliability and reasoning through diverse tests of accuracy, consistency, robustness, and correct reasoning, then improve with grounding, validation, prompt design, and continuous monitoring.

Explore how orchestrated ai agents monitor orders, routes, and inventory to autonomously optimize logistics, reduce delays, and boost customer satisfaction through real-time decision making.

Master agent tool calling by mapping internal REST APIs to JSON schemas for autonomous function invocation; learn how LLMs generate tool calls and how your Python app executes them.

Automate document reading and data extraction with Azure AI Document Intelligence to convert invoices, receipts, forms, and tables into structured data.

Train custom models on your documents to decode specialized layouts that pre-built models miss, leveraging layout intelligence, then evaluate and automate domain-specific document processing.

Explore how Azure AI Search indexes, enriches, and queries large datasets to turn unstructured documents into a searchable knowledge base, and integrate with Power Platform for enterprise-wide intelligent search.

Explore how Power Platform enables non-developers to build AI-powered apps and automations through low-code tools, integrating Azure AI services like document intelligence for practical business solutions.

Explore how Azure AI services are priced on a consumption basis, identify cost drivers like transactions, data size, and complexity, and learn to monitor, optimize, and budget AI expenses.

Digitize 50 years of invoices from legacy paper into digital PDFs using Azure AI Document Intelligence. Index and search the data with Azure AI Search for fast, accurate access.

Implement a grounding and verification layer that enforces mandatory source citations in retrieved context, using system prompting, metadata attribution, and a post-generation verification loop with regex checks.

Master the AI901 exam structure, format, duration, scoring, and question types—multiple choice, multiple select, drag-and-drop, and scenario-based—while applying time-management and elimination strategies.

Master critical thinking for the AI901 exam by using elimination strategies to remove incorrect options, then apply keyword analysis and logical reasoning to choose the best answer.

Develop time management for the AI-901 exam by planning per-question time, using a skip strategy for tough items, answering easy questions quickly, and reserving the final minutes for review.

Outline a day-of mental checklist to boost exam performance through pre-exam preparation, mental readiness, and a calm closing, with quick reviews of key concepts, Azure services, and AI workloads.