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Microsoft Azure AI Fundamentals (AI-900) Certification Prep

Microsoft Azure AI Fundamentals

Website LinkedIn

Short link: go.techtrainertim.com/ai900

Official preparation course for the Microsoft Azure AI Fundamentals (AI-900) certification exam. O'Reilly live training -- 5 hours, 80% demos, 20% theory.

Exam Information

Course Flow (5 Segments)

Segment Topic Time Exam Weight
S1 AI Fundamentals & Responsible AI 09:00 AM 15-20%
S2 Machine Learning 10:00 AM 15-20%
S3 Computer Vision 11:00 AM 15-20%
S4 Natural Language Processing 12:00 PM 15-20%
S5 Generative AI 01:00 PM 20-25% (highest)

For full objective details, see AI-900-exam-objectives.md.

Repository Structure

ai900/
├── .github/                        # GitHub config (CODEOWNERS, templates, Copilot agents)
│   ├── agents/                     # GitHub Copilot agent definitions
│   ├── instructions/               # Copilot custom instructions
│   ├── prompts/                    # Copilot prompt files
│   └── ISSUE_TEMPLATE/
├── bicep/                          # CAF-aligned IaC for lab environment
│   ├── main.bicep
│   ├── modules/                    # ai-services, openai, doc-intel, ML, etc.
│   └── parameters/                 # dev.bicepparam, prod.bicepparam
├── demos/                          # Live class demos (Python + uv)
│   ├── .env.example                # Template for Azure credentials
│   ├── assets/                     # Shared media & datasets (LFS-tracked)
│   │   ├── Audio-Video/
│   │   ├── CSV/
│   │   ├── OCR/
│   │   ├── People/
│   │   ├── Places/
│   │   └── Things/
│   ├── hour-1-ai-fundamentals/     # Vision, Content Safety, Responsible AI
│   ├── hour-2-machine-learning/    # scikit-learn classification/regression/clustering
│   ├── hour-3-computer-vision/     # Image Analysis, OCR, Face, Document Intelligence
│   ├── hour-4-nlp/                 # Sentiment, NER, Speech, CLU
│   └── hour-5-generative-ai/      # GPT-4o, DALL-E 3, prompt engineering, RAG
├── docs/                           # Course materials & exam prep
│   ├── warner-ai900-feb-2026.pptx  # Slide deck (current delivery)
│   ├── AI-900-exam-objectives.md   # Full objective domain
│   ├── AI-900-CORE-RESOURCES.md    # Curated study materials
│   ├── AI-900-PRACTICE-QUESTIONS.md
│   ├── AI-900-PRACTICE-QUESTIONS-SET2.md
│   ├── PRACTICE-QUESTIONS-GUIDE.md # Practice exam resources
│   ├── MCP-DOCS-SERVER-GUIDE.md    # Claude AI + MS Docs for cert prep
│   ├── INDEX.md                    # Guided tour of all materials
│   ├── LEARNING_RESOURCES.md
│   └── exam-metadata/
├── feb-2026/                       # Current delivery course plan
│   ├── course-plan-feb-2026.md
│   └── to-be-archived/            # Legacy content staged for removal
├── images/                         # README cover images
├── practice-questions/             # Practice questions by domain
│   ├── 01-ai-workloads-and-considerations/
│   ├── 02-machine-learning-on-azure/
│   ├── 03-computer-vision-workloads/
│   ├── 04-nlp-workloads/
│   └── 05-generative-ai-workloads/
├── scripts/                        # Utility scripts
│   ├── deploy-ai-services.sh
│   ├── cleanup-ai-services.sh
│   ├── validate-links.py
│   └── github-cli.ps1
└── temp/                           # Temporary working files

Running the Demos

Each demo is a standalone Python project managed with uv. You need Python 3.13+ and uv installed.

cd demos/hour-1-ai-fundamentals
uv sync                   # creates .venv and installs dependencies
uv run python main.py     # launches interactive menu

Repeat for any hour-N-* folder. All demos share a single demos/.env file for Azure credentials:

cp demos/.env.example demos/.env
# Fill in your Azure AI Services keys and endpoints

Bicep Deployment

Deploy the full lab environment with one command:

az deployment group create \
  --resource-group AI900-Feb2026 \
  --template-file bicep/main.bicep \
  --parameters bicep/parameters/dev.bicepparam

Azure Terminology (Current as of May 2025)

The exam uses current Azure service names exclusively. Deprecated names appear only as wrong answers.

Deprecated Name Current Name
Cognitive Services Azure AI Services
LUIS CLU (Conversational Language Understanding)
QnA Maker Custom Question Answering
AI Studio Microsoft Foundry (ai.azure.com)
Language Studio Deprecated -- use Microsoft Foundry portal

Key Azure Portals

Portal URL Notes
Azure Portal portal.azure.com Resource management
Microsoft Foundry ai.azure.com Primary for Language, GenAI, model catalog
Azure ML Studio ml.azure.com AutoML, Designer, endpoints
Vision Studio portal.vision.cognitive.azure.com Image Analysis, OCR, Face
Speech Studio speech.microsoft.com Speech-to-text, text-to-speech
Custom Vision customvision.ai Image classification, object detection

Study Resources

In This Repo

Microsoft Learn Path

Register for Exam

Prerequisites

  • Basic understanding of cloud computing concepts
  • Microsoft Azure subscription (free trial or paid)
  • Python 3.13+ and uv for running demos
  • Interest in artificial intelligence and machine learning

Instructor

License

This course material is licensed under the MIT License. See the LICENSE file for details.