How I Passed the Microsoft Azure AI-102 Exam

Hey folks! As a DevOps engineer with a passion for cloud automation and AI integration, I've been diving deeper into Azure's ecosystem. Recently, on November 10, 2025, I sat for and passed the AI-102: Designing and Implementing a Microsoft Azure AI Solution exam. It was a challenging but rewarding experience that bridged my DevOps skills with AI engineering. If you're a fellow engineer eyeing this certification, this post is for you—I'll share my preparation strategy, resources, and tips to help you ace it.

Why I Pursued AI-102

In my day-to-day role, I automate pipelines, manage infrastructure as code, and integrate services across Azure. AI is becoming integral to modern apps—think chatbots in CI/CD tools, image analysis in deployment monitoring, or natural language processing for log analytics. The AI-102 cert focuses on building and deploying Azure AI solutions, which aligns perfectly with DevOps principles like CI/CD integration, monitoring, and security. Plus, it's a great way to level up in the Azure AI Engineer Associate path. If you have a background in Python/C#, REST APIs, or Azure fundamentals (like AZ-900), this is a natural next step.

Step 1: Exploring the Official Study Guide

The foundation of my prep was the official Microsoft study guide at https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-102. It's updated as of April 30, 2025, with no major changes since then (I double-checked for any November updates—none yet). This guide outlines what to expect: a 120-minute exam with multiple-choice, drag-and-drop, and case studies, needing a 700+ score to pass.

Key highlights:

Audience Profile: For engineers who design, build, deploy, and manage AI solutions using Azure AI services like Vision, Language, Speech, Search, and OpenAI.
Skills Measured (20-25% on planning/managing; 15-20% on generative AI; 5-10% on agentic solutions; 10-15% on computer vision; 15-20% on NLP; 15-20% on knowledge mining).
Changes: Recent updates emphasize Azure AI Foundry, responsible AI, and new sections on agentic solutions and generative AI optimization.

Pro Tip: Start here to map out gaps in your knowledge. The guide links to free practice assessments and the exam sandbox—use them early!

Step 2: Creating a Learning Plan and Managing Time

Consistency is key in DevOps, and the same applies to cert prep. I planned for 2-3 months (about 8-10 weeks), studying 1-2 hours daily and 4-6 hours on weekends. Here's my structured plan, aligned with the exam domains:

Week 1-2: Plan and Manage Azure AI Solutions (20-25%) Focus: Service selection, responsible AI principles, resource creation (via Portal/CLI/ARM), CI/CD integration, monitoring (Azure Monitor, costs), security (Key Vault, Entra ID). Time: 10-15 hours. Hands-on: Set up an Azure AI resource and deploy a simple container.
Week 3-4: Generative AI and Agentic Solutions (20-30% combined) Focus: Building with Azure AI Foundry/OpenAI, prompts, RAG patterns, agents (Semantic Kernel, Autogen), optimization (parameters, fine-tuning). Time: 15-20 hours. Hands-on: Deploy GPT models, create custom agents.
Week 5: Computer Vision Solutions (10-15%) Focus: Image analysis (OCR, object detection), custom models, video insights (Video Indexer, Spatial Analysis). Time: 8-10 hours. Hands-on: Train a Custom Vision model and analyze videos.
Week 6: Natural Language Processing Solutions (15-20%) Focus: Text/speech analysis, translation, custom models (CLU, QnA), intent recognition. Time: 10-12 hours. Hands-on: Build a speech-to-text app and a multi-language QnA bot.
Week 7: Knowledge Mining and Information Extraction (15-20%) Focus: Azure AI Search (indexes, skillsets), Document Intelligence (prebuilt/custom models), content understanding. Time: 10-12 hours. Hands-on: Create a search index with custom skills and extract data from docs.
Week 8-10: Review, Practice, and Mock Exams Time: 20+ hours. Revisit weak areas, take the free practice assessment on Microsoft Learn, and simulate the exam environment.

Tools for time management: I used my custom Obsidian note for tracking progress, set daily Pomodoro sessions (25-min focus bursts), and blocked calendar time. Adjust based on your schedule— if you're full-time working, aim for evenings/weekends to avoid burnout.

Step 3: Key Resources I Used

I curated and relied on a mix of official and community resources. Here's what worked:

My Own Repository: I built https://github.com/amirkhonov/microsoft-ai-102-exam-study-guide based on the skills measured. It organizes everything by domain with links to Microsoft Learn modules, official docs, quickstarts, and hands-on labs. Feel free to fork and contribute!
Mindmap for Visual Learners: The repo at https://github.com/lrivallain/ai-102-mindmap is gold. It's a markmap-based outline covering all skills hierarchically—great for quick reviews. It details services like Azure AI Vision, Speech, Search, and OpenAI, with practical steps (e.g., CLI commands, API examples). This repository can include the outdated/retired services, double-check everything here.
Other Notes and Repos: Check out https://github.com/vatsprat/AI-102-AI-Engineer-Associate-Certification-Exam- for personal notes on clearing the exam. While it's lightweight, it inspired me to jot down my own summaries.
Study Resources from the Guide: Azure docs for each service (e.g., OpenAI, Vision), Microsoft Q&A for doubts, and Tech Community forums.

Step 4: Hands-On Practice with Azure Subscription

Theory alone won't cut it—AI-102 is heavy on implementation. I used my Azure subscription for labs. If you're new, start with the free trial (azure.microsoft.com/free) which gives $200 credit for 30 days. Opt for free tiers where possible (e.g., Azure AI Search free tier, OpenAI playground).

Tips to Control Costs:

Monitor usage in Azure Cost Management—set budgets and alerts.
Delete resources after labs (use Azure CLI: az group delete).
Stick to low-quota deployments (e.g., standard SKU for testing).
I spent about $50 over 2 months, mostly on OpenAI tokens and Vision processing—worth it for real-world skills.

Hands-on examples: Deployed a RAG-based app with Azure AI Foundry, trained custom vision models, and integrated Speech SDK in a Python script. This made concepts like prompt engineering and skillsets stick.

Exam Day Experience and Tips

The exam had ~58 questions, including case studies on end-to-end solutions. It tested practical scenarios: choosing services, securing resources, optimizing generative AI.

Tips:

Focus on Changes: Know the April 2025 updates—e.g., agentic solutions, responsible AI governance.
Practice Responsibly: Questions on content safety, prompt shields, and ethical AI are common.
Time Management: Skip tough ones first; use the extra 30 mins if English isn't your first language.
Utilize Microsoft Learn During the Exam: Since Microsoft Docs and Learn are accessible during the exam (for non-Fundamentals certs like AI-102), familiarize yourself with their structure to quickly search for necessary information on services, APIs, and configurations.

Don't Cram: Review mindmaps the day before, get good sleep.

Post-Exam: Renew annually via free assessments on Learn.

Final Thoughts

Passing AI-102 has already paid off—I'm now integrating AI into my DevOps pipelines more confidently. If you're prepping, start with the official guide, build a plan, and get hands-on. Check my repo for structured resources.

What cert are you chasing next? Let me know in the comments.