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Am I Ready for the AI-102?

Before diving into the challenges, take a few minutes to assess your readiness. The AI-102 is an Associate-level exam that assumes hands-on programming experience and familiarity with AI concepts. Unlike AI-900, this exam requires you to write code that integrates Azure AI services into applications.

Self-Assessment Checklist

Click each row to cycle through: ✅ Comfortable | ⚠️ Need Review | ❌ New to Me

Programming Skills

SkillYour Level (click to rate)
I can write Python or C# code confidently
I understand REST APIs and HTTP requests
I can read and write JSON
I have used SDKs to call cloud services
I understand async/await patterns
I know how to use environment variables and configuration
I can deploy containers (Docker basics)

AI & Azure AI Experience

SkillYour Level (click to rate)
I have used Azure AI services or Cognitive Services
I understand what embeddings and vectors are
I know the RAG (Retrieval-Augmented Generation) pattern
I have worked with Azure OpenAI or OpenAI API
I understand NLP concepts (tokenization, NER, sentiment)
I have built or used a search index
I know what prompt engineering means

How to Interpret Your Results

Mostly ✅: You're ready!

Jump straight to Lab Setup and start Challenge 01.

Mix of ✅ and ⚠️: You're almost ready

Start the challenges but budget 2–4 weeks of focused study. Use the Learning Resources links in each challenge to fill gaps as you go.

Several ❌: Start with fundamentals first

Consider these resources before starting AI-102:

No AI experience at all?

That's okay! The AI-900 (Azure AI Fundamentals) certification is an excellent starting point. It covers the concepts behind AI services without requiring code. Many candidates take AI-900 first, build programming skills, then tackle AI-102.

Experience Expectations

According to Microsoft, AI-102 candidates should have:

  • 1+ years of experience with Python or C#
  • Familiarity with REST APIs and SDKs
  • Understanding of Azure services (subscriptions, resource groups, deployment)
  • Knowledge of AI/ML fundamentals (training, inference, models, evaluation)
  • Experience with DevOps basics (Git, CI/CD concepts, containers)
Don't have a full year of experience?

These challenges are designed to accelerate your learning. Each challenge is self-contained with Python, C#, and REST tabs. If you're a fast learner and can code confidently, you can build equivalent hands-on experience by completing all 49 challenges in 4–6 weeks of focused study.


Ready to go? Head to the Lab Setup to configure your environment, then start with Challenge 01.