Microsoft Certified: Azure AI Engineer Associate (AI‑102) Practice Exams
About the Azure AI-102 exam
AI-102 retirement (June 2026)
Microsoft retires AI-102 and the entire Azure AI Engineer Associate certification on 30 June 2026. No successor exam at the Associate tier has been announced. Anyone currently certified retains the credential until their 12-month renewal cycle ends; renewals stop being offered after June 30, 2026. New candidates should consider AI-900 (Foundations — transitioning to AI-901) or AZ-204 Azure Developer for broader development depth that overlaps with AI service integration. The practice material below reflects AI-102 as-released; useful if you're attempting the exam before retirement.
Exam at a glance
Microsoft's associate-tier certification for AI engineers building production AI solutions on Azure.
Skills measured
- Plan and manage an Azure AI solution — 15–20%
- Implement generative AI solutions — 15–20%
- Implement an agentic solution — 10–15%
- Implement computer vision solutions — 15–20%
- Implement natural language processing solutions — 15–20%
- Implement knowledge mining and document intelligence — 10–15%
Core services tested
- Azure AI Foundry — portal + SDK for managing AI projects, model catalog, prompt flow, evaluations.
- Azure OpenAI Service — model deployment, embeddings, content filtering, RBAC, networking, prompt engineering patterns.
- Azure AI Vision & Custom Vision — image analysis, object detection, OCR, custom image classification + detection models.
- Azure AI Document Intelligence — prebuilt and custom models for layout, invoices, receipts, IDs.
- Azure AI Language — entity recognition, sentiment, custom text classification, custom NER, conversational language understanding.
- Azure AI Speech — speech-to-text, text-to-speech, custom neural voice, speaker recognition.
- Azure AI Search — indexes, indexers, semantic ranking, vector search for RAG patterns.
- Azure AI Content Safety — content filtering, prompt shields, groundedness detection.
Prerequisites
No formal prerequisites. Microsoft recommends hands-on experience developing solutions in Python or C#, comfort calling REST APIs and SDKs, and a working understanding of responsible AI principles. Passing AI-900 first is strongly recommended for non-developers entering the AI space.
Why take this certification
- Most-requested Azure AI credential. AI-102 is the only Microsoft certification dedicated to building production AI applications on Azure, and it is increasingly listed as a requirement on cloud AI engineering job postings.
- Competitive salary. Azure AI engineers in the United States typically earn $130,000–$160,000 USD per year, with senior generative-AI specialists trending higher as enterprises scale Azure OpenAI deployments.
- Aligned with the generative-AI shift. The most recent AI-102 update added dedicated skill areas for generative AI and agentic solutions — reflecting how Azure customers actually build with AI Foundry, Azure OpenAI, and the Agent Service today.
- Free annual renewal. Unlike one-shot exams that require a full retake every few years, AI-102 renews via a short online assessment on Microsoft Learn at no cost — keeping your credential aligned with the latest Azure AI surface.
Note: Microsoft has announced this certification will retire on June 30, 2026. Verify status on the official certification page before booking.
What you'll learn in the AI-102 exam
AI-102 targets developers and AI engineers building production AI solutions on Azure. The exam emphasizes Azure AI Foundry, Azure OpenAI, custom vision and language solutions, and integrating AI capabilities into applications — with a strong responsible-AI thread running through every skill area.
Azure AI Foundry and project management
- Provision and configure Azure AI services (single-service vs multi-service resources, keys, endpoints, custom domains).
- Use the Azure AI Foundry portal and SDK to create projects, deploy models from the catalog, and run prompt flow evaluations.
- Secure AI workloads with managed identity, RBAC, private endpoints, customer-managed keys, and network isolation.
- Monitor cost, throughput, and latency with Azure Monitor, diagnostic logs, and quota dashboards.
Generative AI and agents
- Deploy Azure OpenAI models — chat, embeddings, image generation — and tune deployment SKUs for throughput.
- Apply prompt engineering, few-shot patterns, structured outputs, and function/tool calling.
- Build agents with the Agent Service in AI Foundry, including connecting tools and using MCP for tool integration.
- Apply content filtering, prompt shields, and groundedness detection via Azure AI Content Safety.
Computer vision
- Use Azure AI Vision for image analysis, OCR, and spatial analysis scenarios.
- Train and deploy Custom Vision classification and object-detection models.
- Implement Face API responsibly within Microsoft's gated-access guardrails.
- Extract structured data with Azure AI Document Intelligence (prebuilt + custom models).
Natural language processing
- Use Azure AI Language for entity recognition, key phrase extraction, sentiment, and PII detection.
- Build custom text classification and custom NER models.
- Translate text and speech with Azure AI Translator.
- Implement speech-to-text, text-to-speech, and custom neural voice with Azure AI Speech.
Knowledge mining and RAG
- Build Azure AI Search indexes with skillsets, indexers, and cognitive enrichment.
- Implement semantic ranking and vector search to power retrieval-augmented generation.
- Combine Azure AI Search with Azure OpenAI to ground LLM responses in enterprise data.
How the practice exams help
Each free question and every premium exam mirrors the scenario-style format Microsoft uses — workload constraints, multiple correct answers in some items, drag-and-drop sequences, and code snippets where you choose the right SDK call. Detailed explanations cover not just why the right answer is right but why the distractors are wrong, so you learn Azure's AI surface rather than memorizing answers.
How to prepare for the AI-102 exam
A successful AI-102 preparation strategy combines theoretical study with substantial hands-on time on Azure AI Foundry and Azure OpenAI. Recommended approach:
- Establish fundamentals (1 week). If you have not already passed AI-900, work through the AI-900 learning path first. It locks in vocabulary (responsible AI, common AI workloads, Azure AI service families) that AI-102 assumes.
- Study the official AI-102 learning path (3–4 weeks). Review the official AI-102 study guide and work through the modules on Microsoft Learn — the official content closely mirrors the exam blueprint.
- Hands-on with Azure AI Foundry and Azure OpenAI (2–3 weeks). Create an Azure free-tier subscription and build real solutions: deploy a gpt-4 model in Azure OpenAI, build a RAG pipeline with Azure AI Search, train a Custom Vision detector, fine-tune a custom NER model, and wire up an agent in AI Foundry. The exam tests workflow knowledge that only hands-on use produces.
- Practice exams (1–2 weeks). Take timed practice tests to identify weak skill areas. Microsoft's free practice assessment is a useful baseline; supplement with third-party question banks for breadth. Aim for consistent 80%+ scores before scheduling.
Recommended timeline
6–10 weeks of focused study (10–15 hours per week) for developers with prior Azure experience. Engineers new to Azure should allow 10–14 weeks and pass AI-900 first.
Official resources
Bookmark the official AI-102 certification page and download the linked study guide. Microsoft's free Learn training hosts the official AI Engineer learning path, and the Microsoft Learn exam sandbox lets you experience the actual exam interface before exam day.