Microsoft Azure AI Engineer AssociateStudy guide
The associate certification for designing and implementing AI solutions with Azure AI services (AI-102).
About Microsoft Azure AI Engineer Associate (AI-102)
Microsoft Azure AI Engineer Associate (AI-102) is a Associate-level certification from Microsoft. This page organizes the exam scope into a 5-chapter, 11-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Plan and manage an Azure AI solution~22%
- Implement generative AI solutions~18%
- Implement computer vision solutions~14%
- Implement natural language processing solutions~18%
- Implement knowledge mining and information extraction~18%
- Implement an agentic solution~10%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Note: This exam is scheduled to retire on 2026-06-30 (successor: AI-103).
Official exam information: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-102
1Plan and Manage an Azure AI Solution
- 1.1Azure AI Services and Creating Resources
Understand the overview of Azure AI services (formerly Cognitive Services) as the foundation for AI solutions, creating resources, and consuming them via endpoints and keys. The starting point for "Plan and manage an Azure AI solution" in AI-102.
- 1.2Responsible AI and Content Safety
Understand Microsoft’s Responsible AI principles (fairness; reliability & safety; privacy & security; inclusiveness; transparency; accountability) and Azure AI Content Safety for detecting harmful content. The basis for operating AI safely.
- 1.3Securing, Monitoring, and Deploying AI Solutions
Understand operations for AI resources: security (key management, network restrictions, Entra/managed identity), monitoring (metrics, diagnostic logs, cost), and running in containers on-prem/edge.
2Implement Computer Vision Solutions
- 2.1Image Analysis and OCR (Azure AI Vision)
Understand Azure AI Vision image analysis (tags, object detection, captions) and OCR (Read) for text recognition that extract information from images—the core pre-built computer vision capabilities.
- 2.2Custom Vision and Face Analysis
Understand Custom Vision for training your own image classification/object-detection models, and Azure AI Face for face detection, attributes, and matching. Use custom models when pre-built isn’t enough.
3Implement Natural Language Processing Solutions
- 3.1Text Analysis with Azure AI Language
Understand the pre-built features of Azure AI Language that extract meaning from text—sentiment analysis, key phrase extraction, named entity recognition (NER), language detection, PII detection, and summarization. The basis of NLP.
- 3.2Conversational Language, Translation, and Speech
Understand Conversational Language Understanding (CLU) for intent/entity prediction, Translator for text/document translation, and Azure AI Speech for speech-to-text and text-to-speech. Applied NLP for dialogue, multilingual, and voice.
4Implement Knowledge Mining and Information Extraction
- 4.1Knowledge Mining with Azure AI Search
Understand Azure AI Search, which builds a searchable index from large document sets. Learn indexers, skillsets (AI enrichment), indexes, and the basics of vector search / RAG that pair with generative AI.
- 4.2Information Extraction with Document Intelligence
Understand Azure AI Document Intelligence (formerly Form Recognizer), which extracts structured data (key/value, tables, fields) from documents like forms, invoices, receipts, and IDs. Choose between prebuilt and custom models.
5Implement Generative AI and Agentic Solutions
- 5.1Azure OpenAI, Prompts, and RAG
Understand Azure OpenAI Service for using large language models on Azure, prompt engineering, and RAG (retrieval-augmented generation) for answers grounded in your own data—the core of building generative AI.
- 5.2Agentic Solutions and Orchestration
Understand agentic solutions where an LLM calls tools (functions) and autonomously executes multi-step tasks. Learn function calling (tools), Azure AI Foundry / Agent Service, and orchestration concepts.