intermediateCertificateFree

Azure AI Engineer Associate

Name: Azure AI Engineer Associate
Rating: 4.4 (4800 reviews)

by Microsoft AI Team · Microsoft Learn

4.4

(4,800 reviews)

200K+ enrolled10 hoursUpdated 2025-01

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Our Verdict

Worth it — with caveats

This is Microsoft's free, self-paced official training for the AI-102 'Designing and Implementing a Microsoft Azure AI Solution' certification, and the verdict is conditional and time-sensitive: the content is solid and is the standard prep route that real passers used, but the AI-102 exam and the Azure AI Engineer Associate certification both retire on June 30, 2026, and are being replaced by the new AI-103 (Azure AI App and Agent Developer Associate), whose beta opened April 21, 2026. Because developers who passed report roughly 8 weeks to 6 months of study, almost no one starting this in mid-2026 can realistically certify before retirement, so if your goal is the credential you should target AI-103 instead. The training itself is structured as six learning paths (Foundry/Azure AI services, computer vision, NLP, generative AI with Azure OpenAI, knowledge mining with Azure AI Search, and document intelligence) and is genuinely good for hands-on Azure AI skills regardless of the exam. Note that the official skills measured were refreshed on December 23, 2025 to emphasize generative AI and a new 'Implement an agentic solution' domain, and the catalog's single 10-hour learning-path link no longer resolves because Microsoft restructured the paths. Treat the catalog's 4.4 rating as unverified; we could not find a public star-rating source for the official Microsoft Learn path.

The free official Microsoft Learn content is high quality and is the route real candidates used to pass AI-102, but the certification and exam retire on June 30, 2026 (about two weeks after this review) and are superseded by AI-103. Anyone whose goal is the credential should pivot to AI-103; the training only makes sense now for free, Azure-specific skill-building or for someone already exam-ready who can sit AI-102 before the deadline.

Best for: Developers (Python or C#) already working in Microsoft/Azure shops who want hands-on, no-cost practice with Azure AI services (Azure OpenAI/Foundry, Azure AI Vision, Speech, Language, AI Search, Document Intelligence) via REST APIs and SDKs. It also suits someone who is already near exam-ready and can schedule and pass the AI-102 exam before its June 30, 2026 retirement, or anyone who simply wants the free official curriculum as a structured Azure AI learning track independent of the cert.

Skip if: Anyone starting from scratch in mid-2026 who wants the certification, since AI-102 retires June 30, 2026 and realistic prep takes weeks to months (target AI-103 instead). Also not ideal for complete AI beginners (the AI-900 Azure AI Fundamentals path or a conceptual course is a better on-ramp), for people who don't work in the Azure ecosystem (the skills are Azure-service-specific, not transferable ML theory), or for those wanting deep ML/math foundations rather than applied cloud-service integration.

About This Course

Prepare for AI-102 certification covering Azure Cognitive Services, Bot Service, and Azure Machine Learning deployments.

What You'll Learn

Plan, create, deploy, secure, and monitor Azure AI / Microsoft Foundry services, including cost management, key protection, authentication, and CI/CD + container deployment (20-25% of the exam)

Build generative AI solutions with Microsoft Foundry and Azure OpenAI: deploy models, implement prompt flow and RAG (grounding on your data), evaluate flows, apply prompt engineering, and fine-tune models (15-20%)

Implement an agentic solution using Microsoft Foundry Agent Service and the Microsoft Agent Framework, including multi-agent orchestration (5-10%, a domain added in the Dec 2025 refresh)

Implement computer vision solutions: image analysis and tagging, OCR, custom image classification/object detection, and video insights with Azure AI Video Indexer (10-15%)

Implement NLP solutions: key-phrase/entity/sentiment/PII/language detection, text and document translation, speech-to-text and text-to-speech with SSML, and custom language understanding and question answering (15-20%)

Implement knowledge mining and information extraction with Azure AI Search (indexes, skillsets, custom skills, semantic and vector search) and Azure Document Intelligence, plus Content Understanding pipelines (15-20%)

Apply and configure responsible AI: content filters, blocklists, prompt shields, harm detection, and a responsible AI governance framework

Curriculum

Plan and manage an Azure AI solution (20-25%)

Select appropriate Microsoft Foundry / Azure AI services; create and deploy AI resources and models; install SDKs and APIs; integrate into CI/CD and container deployments; monitor, manage costs, protect keys, and handle authentication; implement responsible AI (content moderation, content safety, content filters, blocklists, prompt shields, governance).

Implement generative AI solutions (15-20%)

Build generative AI with Microsoft Foundry (hubs, projects, RAG grounding, prompt flow, model/flow evaluation, prompt templates, Foundry SDK); use Azure OpenAI for text, code, DALL-E images, and multimodal models; optimize and operationalize (tuning parameters, monitoring, scaling, orchestration of multiple models, prompt engineering, fine-tuning).

Implement an agentic solution (5-10%)

Understand agent use cases; configure resources and build agents with the Microsoft Foundry Agent Service; implement complex agents with the Microsoft Agent Framework; orchestrate multi-agent, multi-user, and autonomous workflows; test, optimize, and deploy agents. (New domain added in the December 23, 2025 skills update.)

Implement computer vision solutions (10-15%)

Analyze images (object detection, tagging, feature selection, OCR including handwriting via Azure Vision); build and consume custom vision models (image classification vs object detection, labeling, training, evaluation, code-first); analyze video and live streams with Azure AI Video Indexer and Spatial Analysis.

Implement natural language processing solutions (15-20%)

Analyze and translate text (key phrases, entities, sentiment, language and PII detection, Translator); process and translate speech (text-to-speech and speech-to-text, SSML, custom speech, intent/keyword recognition); build custom language understanding models and custom/multi-turn question answering knowledge bases; implement custom translation.

Implement knowledge mining and information extraction solutions (15-20%)

Build Azure AI Search solutions (indexes, indexers, data sources, custom skills/skillsets, querying with filtering/sorting/wildcards, Knowledge Store projections, semantic and vector search); build Azure Document Intelligence solutions (prebuilt, custom, and composed models); extract information with Azure Content Understanding (OCR pipelines, classification, entity/table/image extraction across documents, images, video, and audio).

Prerequisites

Programming experience in Python or C# (the exam and labs assume you can write code)
Ability to call REST APIs and use SDKs to integrate cloud services
Familiarity with the Azure portal and core Azure AI portfolio plus available data-storage options
Understanding of responsible AI principles (content moderation, content safety, governance)
Recommended but not required: Azure AI Fundamentals (AI-900) level knowledge as a foundation

Instructor

Microsoft AI Team

Instructor · Microsoft Learn

Pros & Cons

Pros

Completely free and self-paced on Microsoft Learn, with hands-on labs that simulate real Azure scenarios; multiple developers who passed cite it as their primary study source
Directly authored by Microsoft and mapped to the official exam objectives, and was refreshed on December 23, 2025 to add a generative-AI and agentic-AI focus, so the technical content is current
Broad, practical coverage of the full applied Azure AI stack (Azure OpenAI/Foundry, Vision, Speech, Language, AI Search, Document Intelligence) using real SDKs and REST APIs rather than abstract theory
Strong on responsible AI in practice (content filters, blocklists, prompt shields, harm detection, governance), which is increasingly required in enterprise AI work
Skills transfer to the successor AI-103 path, so time spent is not wasted even though AI-102 itself is retiring

Cons

The AI-102 exam and certification retire on June 30, 2026, so the credential is effectively end-of-life; new starters should target the replacement AI-103 instead
The catalog's single 10-hour learning-path URL no longer resolves and the figure understates scope, prep is actually six learning paths and passers report ~8 weeks to 6 months of study plus extensive hands-on practice
Real candidates report the free content alone was not fully sufficient, they supplemented with practice tests (e.g. SkillCertPro) and external videos (e.g. John Savill), and found Microsoft Learn slow to navigate
Azure-service-specific and assumes Python/C# plus existing Azure familiarity, it teaches platform usage, not foundational ML/AI theory, so it has limited value outside the Microsoft ecosystem

Alternatives To Consider

Azure AI Fundamentals

Microsoft Learn

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Generative AI with Large Language Models

Coursera

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LangChain for LLM Application Development

DeepLearning.AI

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Frequently Asked Questions

Is Azure AI Engineer Associate free?

Yes — Azure AI Engineer Associate is free to access. The Microsoft Learn training is free. The AI-102 certification exam costs roughly USD 165 (price varies by country/region) and is scheduled through Pearson VUE; passing score is 700/1000 with a 100-minute proctored exam. The exam retires June 30, 2026, so this fee only makes sense if you can sit it before that date; otherwise budget for the successor AI-103 exam instead.

Who is Azure AI Engineer Associate for?

Developers (Python or C#) already working in Microsoft/Azure shops who want hands-on, no-cost practice with Azure AI services (Azure OpenAI/Foundry, Azure AI Vision, Speech, Language, AI Search, Document Intelligence) via REST APIs and SDKs. It also suits someone who is already near exam-ready and can schedule and pass the AI-102 exam before its June 30, 2026 retirement, or anyone who simply wants the free official curriculum as a structured Azure AI learning track independent of the cert.

What will you learn in Azure AI Engineer Associate?

Plan, create, deploy, secure, and monitor Azure AI / Microsoft Foundry services, including cost management, key protection, authentication, and CI/CD + container deployment (20-25% of the exam); Build generative AI solutions with Microsoft Foundry and Azure OpenAI: deploy models, implement prompt flow and RAG (grounding on your data), evaluate flows, apply prompt engineering, and fine-tune models (15-20%); Implement an agentic solution using Microsoft Foundry Agent Service and the Microsoft Agent Framework, including multi-agent orchestration (5-10%, a domain added in the Dec 2025 refresh); Implement computer vision solutions: image analysis and tagging, OCR, custom image classification/object detection, and video insights with Azure AI Video Indexer (10-15%).

What are the prerequisites for Azure AI Engineer Associate?

Programming experience in Python or C# (the exam and labs assume you can write code); Ability to call REST APIs and use SDKs to integrate cloud services; Familiarity with the Azure portal and core Azure AI portfolio plus available data-storage options; Understanding of responsible AI principles (content moderation, content safety, governance); Recommended but not required: Azure AI Fundamentals (AI-900) level knowledge as a foundation.

Is Azure AI Engineer Associate worth it?

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on Microsoft Learn's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

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