7-Day Crash Course Plan for AI-900 Certification Preparation

The AI-900: Microsoft Azure AI Fundamentals exam is designed to validate conceptual clarity—not deep coding expertise. That sounds simple, but many candidates underestimate it and end up over-preparing in the wrong direction.
A 7-day plan works—if executed with precision. This is not about covering everything. It’s about covering the right things efficiently.
Let’s structure it like a sprint.
Day 1 — Build the Foundation: What is AI on Azure?
Start with the basics on Microsoft Learn.
Focus on:
• What is Artificial Intelligence?
• Types of AI workloads
• Responsible AI principles
Also understand the role of Microsoft Azure in delivering AI services.
Outcome:
You should be able to explain AI concepts in simple terms—like you’re teaching a non-technical stakeholder.
Day 2 — Machine Learning Essentials (Don’t Overcomplicate)
You’re not training models—but you need to understand how they work.
Cover:
• Regression vs classification
• Clustering basics
• Training vs inference
• Overfitting
Use tools like Azure Machine Learning conceptually—no need for deep implementation.
Outcome:
Clarity on when to use ML—not how to code it.
Day 3 — Computer Vision
Shift focus to image-based AI.
Learn:
• Image classification
• Object detection
• Optical Character Recognition (OCR)
Explore services like Azure AI Vision.
Pro tip:
Don’t just read—upload an image and test outputs. Even 20 minutes of hands-on beats hours of reading.
Outcome:
You should understand what each vision capability does and where it’s applied.
Day 4 — Natural Language Processing (High Weightage Area)
This is a critical domain for AI-900.
Focus on:
• Sentiment analysis
• Entity recognition
• Language detection
• Text summarization
Work with Azure AI Language.
Also explore conversational AI basics via Azure Bot Service.
Outcome:
Ability to map business use cases (chatbots, analytics) to NLP services.
Day 5 — Generative AI & Azure OpenAI
Now move to modern AI capabilities.
Understand:
• What is generative AI
• Use cases (text, code, images)
• Prompt engineering basics
Learn how Azure OpenAI Service enables access to models like GPT.
Keep it conceptual:
• Inputs → Prompts
• Outputs → Generated responses
Outcome:
Clear understanding of how generative AI fits into business solutions.