Azure AI Engineer (AI-102) Study Plan 2026: 8 Weeks to Certification

Microsoft refreshed AI-102 in early 2026 to fold Azure AI Foundry (the successor to Azure AI Studio) and expanded Azure OpenAI content. The exam validates that you can plan, deploy, monitor and secure AI solutions using Azure Cognitive Services and Azure OpenAI. Roughly 40 to 45 items in 100 minutes, with case studies and drag-and-drop questions.

• Plan and manage Azure AI solutions: 15 to 20 percent
• Implement generative AI solutions with Azure OpenAI: 20 to 25 percent
• Implement an agentic solution with Azure AI Foundry: 10 to 15 percent
• Implement Computer Vision solutions: 10 to 15 percent
• Implement Natural Language Processing solutions: 15 to 20 percent
• Implement knowledge mining and Document Intelligence solutions: 10 to 15 percent

Set up an Azure subscription with free credits. Create your first Azure AI Foundry project. Understand hub vs project, connections, deployments, prompt flow and evaluation. Learn how AI Foundry ties together model catalog, playground, tracing and content safety.

Cover fundamentals: responsible AI principles, cost management, private endpoints, managed identities, key vault integration and role-based access for AI resources.

Deploy GPT-4o or GPT-5.1 in your OpenAI resource. Practise chat completion, embeddings, function calling and structured output. Build a Retrieval Augmented Generation solution using Azure AI Search as the retriever, chunk your corpus, generate embeddings, and answer questions grounded in your data.

• Difference between fine-tuning and RAG and when to use each
• Vector search vs hybrid search vs semantic ranking in AI Search
• Content filters, prompt shields and jailbreak protection
• Provisioned throughput units vs pay as you go pricing

Cover the classic Cognitive Services surface: Language (sentiment, NER, key phrase, PII, custom classification), Speech (speech to text, text to speech, translation, custom neural voice), Vision (image analysis, OCR, custom image classification, face) and Document Intelligence (prebuilt invoice, receipt, ID, layout and custom extraction models). One hands on lab per service.

Take 2 full timed MeasureUp practice tests, review every wrong answer and revisit Microsoft Learn modules for weak areas. Cover content safety in depth: harm categories, severity levels, blocklists and shield APIs. Cover monitoring and tracing in Foundry: how to see prompt inputs, outputs, latencies and cost per request.

Choosing between AI Search semantic ranking and vector search: semantic ranking reranks keyword results, vector search finds semantically similar chunks, hybrid combines both. Choosing between fine-tuning and RAG: use RAG for factual grounding on your data, use fine-tuning to change style, tone or format. Choosing the right Document Intelligence model: prebuilt for known document types, custom for your own templates, layout for tables and structure only.

Across the last twelve months of cohorts, the professionals who made Azure AI Engineer (AI-102) Study Plan 2026 pay off shared three habits. They booked a target exam or milestone date before they felt ready, they blocked two fixed weekday evenings plus one weekend morning on the calendar, and they wrote a one page brief for their manager explaining how the data-ai work connected to a live project or hiring gap. That last step is what turned the certificate from a personal line item into a visible business result, which is what unlocks the promotion, the internal move or the salary conversation later.

The learners who struggled almost always skipped the same things. They studied passively without timed practice, they never explained the material out loud to a peer, and they treated the data & ai plan as a solo pursuit instead of a small accountability pod of three or four people. If you are building your own plan, borrow the habits from the first group and design out the failure patterns from the second. It sounds obvious on paper, but the compounding effect over eight to twelve weeks is the difference between finishing strong and quietly abandoning the goal in month two.