AI-300 Study Guide 2026: Complete Azure AI Engineer Exam Prep

• • Select the appropriate Azure AI service for a given use case
• • Plan and configure Azure AI Foundry hubs and projects
• • Implement access control — managed identities, RBAC, key-based vs. Entra ID authentication
• • Monitor Azure AI services — Azure Monitor, diagnostic logs, alerts, Application Insights
• • Implement responsible AI principles — fairness, reliability, privacy, transparency, accountability
• • Content filtering and Responsible AI dashboards in Azure AI Foundry
• • Cost management — commitment tiers, quotas, throttling strategies

• • Azure Anomaly Detector — univariate vs. multivariate detection, streaming vs. batch
• • Azure Metrics Advisor — data feeds, anomaly alerts, feedback loops
• • Azure Content Safety — text and image harm categories, severity levels, custom blocklists
• • Integrating decision APIs into applications via REST and SDKs

Study tip: This is the smallest domain — understand the purpose and basic API patterns of each service; deep implementation detail is tested more in other domains.

• • Azure AI Vision — image analysis (tags, captions, objects, brands, faces), spatial analysis
• • Custom Vision — image classification (multi-class, multi-label) vs. object detection models
• • Training Custom Vision models — dataset requirements, iterations, export formats (ONNX, Docker)
• • Azure AI Face — face detection, verification, identification, liveness detection
• • Azure Video Indexer — video analysis, transcription, scene detection, people tracking
• • Computer vision with Florence-based models in Azure AI Foundry

Study tip: Know when to use Azure AI Vision (pre-built) vs. Custom Vision (custom training) — the decision scenario appears repeatedly on the exam.

• • Azure AI Language — sentiment analysis, key phrase extraction, entity recognition, opinion mining
• • Custom text classification and custom named entity recognition (NER)
• • Conversational Language Understanding (CLU) — intents, entities, utterances (replacement for LUIS)
• • Question answering (QnA Maker successor) — knowledge bases, multi-turn conversations, active learning
• • Azure AI Translator — text translation, document translation, custom translator
• • Azure AI Speech — STT, TTS, speaker recognition, real-time vs. batch transcription
• • Custom Speech — acoustic models, language models, pronunciation models
• • Speech translation and keyword recognition

Study tip: The CLU (Conversational Language Understanding) service replaced LUIS — make sure you are studying the current service, not the deprecated one.

• • Azure AI Search — index schema, indexers, skillsets, semantic ranking, vector search
• • Custom skills for AI Search — Azure Function as enrichment pipeline step
• • Knowledge store — projections to Blob Storage and Table Storage
• • Azure AI Document Intelligence — pre-built models (invoice, receipt, ID, tax form) vs. custom models
• • Document Intelligence custom models — template vs. neural, composed models
• • Integrating Document Intelligence output with downstream AI pipelines

Study tip: Azure AI Search with vector search and semantic ranking is heavily tested in the context of RAG pipelines — study this alongside the generative AI domain.

• • Azure OpenAI Service — model deployment (GPT-4o, GPT-4, text-embedding-ada-002), API usage, chat completions
• • Prompt engineering — system messages, few-shot examples, temperature, top-p, max tokens
• • RAG (Retrieval-Augmented Generation) — Azure AI Search as grounding data source, on-your-data API
• • Azure AI Foundry — hub/project structure, deployments, evaluations, prompt flows
• • Prompt flow — flow types (standard, chat, evaluation), connections, compute sessions
• • Responsible AI for generative AI — content filters (hate, sexual, violence, self-harm), jailbreak protection, groundedness evaluation
• • Azure AI Agent Service — agents, tools, function calling, code interpreter, file search
• • Embeddings — vector representations, similarity search, chunking strategies
• • Fine-tuning vs. RAG trade-offs — when to use each approach

Study tip: This domain is 20–25% of the exam and grows every cycle. Spend at least one-third of your hands-on lab time in Azure AI Foundry building and evaluating prompt flows and RAG solutions.

1. Microsoft Learn AI-300 Learning Path (Free)

The official learning path is the most current resource and the only one guaranteed to cover all exam objectives including Azure AI Foundry and the latest generative AI content. Complete every hands-on exercise — many questions on the exam are drawn directly from the lab scenarios. The Azure AI Foundry and Azure OpenAI modules are essential.

2. Azure AI Foundry Documentation and Samples

For the generative AI domain, the Azure AI Foundry documentation and the Azure OpenAI samples repository on GitHub are invaluable. The RAG solution tutorial (integrating Azure AI Search with Azure OpenAI via the on-your-data API) covers approximately 15% of exam questions by itself. Build it from scratch at least once.

3. MSCertQuiz AI-300 Practice Tests

500 AI-300 practice questions across all six domains with detailed explanations, including extensive generative AI coverage. Service selection scenarios — the most common question type on the real exam — are well-represented across all domains.

4. John Savill's Azure AI Study Cram (YouTube)

Savill's video on Azure AI covers the service landscape clearly. For the generative AI domain specifically, look for his content on Azure OpenAI, prompt flow, and Azure AI Foundry — his architecture diagrams for RAG pipelines are particularly useful for remembering how components connect in scenario questions.

5. Azure AI Services Quickstarts

For each major service (Language, Speech, Vision, Document Intelligence), run the Python or C# quickstart from the Azure documentation. The time investment is 15–30 minutes per service and builds the hands-on familiarity that multiple-choice questions reward. Pay attention to SDK initialisation, authentication patterns, and response object structures.