Study guide
Current AI-103 Exam Facts
Microsoft Azure AI Apps and Agents Developer Associate AI-103
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1 AI-103 Blueprint, Microsoft Foundry, and Solution Planning
Current AI-103 Exam Facts Foundry Hubs, Projects, Models, and Deployments Security, Networking, Monitoring, and Responsible AI
2 Generative AI, Agents, and Retrieval-Augmented Generation
Prompt Design, Model Selection, and Structured Outputs RAG, Grounding, Embeddings, and Azure AI Search Agent Tools, Functions, Memory, and Evaluation
3 Vision, Language, Information Extraction, and Final Review
Vision, Multimodal, Speech, and Language Workloads Image and Video Generation, Editing, and Content Understanding Pipelines Document Intelligence, Content Understanding, and Extraction AI-103 Study Plan, Traps, and Practice Strategy
Current AI-103 Exam Facts
Key Takeaways Explain This Topic AI
AI-103 is Microsoft's Developing AI Apps and Agents on Azure exam for the Azure AI Apps and Agents Developer Associate credential; Microsoft's exam page shows no beta label (checked October 26, 2026).
Microsoft Learn lists the current skills measured as of April 16, 2026, with Plan and manage an Azure AI solution weighted 25-30% and the generative AI and agentic domain weighted 30-35%.
The passing score is 700/1000 and the credential page states you have 120 minutes for the assessment, scheduled through Pearson VUE.
The official AI-103 Practice Assessment is available on Microsoft's AI Skills Navigator.
Use Microsoft Learn, the local exam metadata, and the AI-103 cheat sheet together so duration, blueprint weights, and Foundry terminology stay aligned during future updates.
Last updated: October 2026
Current AI-103 Exam Facts
AI-103: Developing AI Apps and Agents on Azure validates whether an Azure AI engineer can plan, build, deploy, and operate AI apps and agents that use Microsoft Foundry . The credential page names the certification Microsoft Certified: Azure AI Apps and Agents Developer Associate and lists a 700 passing score.
As of October 26, 2026, Microsoft's exam page shows no beta label, offers scheduling through Pearson VUE, and links the official Practice Assessment on AI Skills Navigator.
Use the April 2026 blueprint as the study anchor. Microsoft Learn states that skills are measured as of April 16, 2026 . That matters because older AI-102 habits do not fully map to AI-103: Foundry projects, agents, model deployments, retrieval, observability, and responsible AI controls are now first-class planning topics.
- Fact Current working value Source note Study implication
- Exam AI-103 Microsoft Learn credential page Live exam; official Practice Assessment on AI Skills Navigator
Passing score 700/1000 Microsoft Learn credential page Treat 700 as a scaled score, not a raw percentage target
- Duration 120 minutes Microsoft Learn credential page Build stamina for scenario and interactive items
- Questions 40-60 typical Local metadata Practice mixed scenarios with time pressure
Scheduling Pearson VUE; price varies by region Microsoft Learn credential page Confirm price and language when booking
Blueprint date April 16, 2026 Microsoft Learn study guide Prefer current Foundry terminology over retired service names
Current Blueprint Weights
- Domain Weight
- Plan and manage an Azure AI solution 25-30%
- Implement generative AI and agentic solutions 30-35%
- Implement computer vision solutions 10-15%
- Implement text analysis solutions 10-15%
- Implement information extraction solutions 10-15%
The heaviest domain is Implement generative AI and agentic solutions (30-35%) , so spend the most time there; Plan and manage (25-30%) is the second-largest slice and is exactly what this chapter covers. The local metadata, AI-103 cheat sheet, and Microsoft Learn blueprint are aligned on this April 16, 2026 weighting model. Future updates should keep those files synchronized before new practice, flashcard, cheat sheet, or guide content is generated.
How AI-103 Differs from AI-102
AI-103 is the successor to AI-102 (Azure AI Engineer Associate). The roles overlap, but the framing shifted decisively toward generative AI and agents . AI-102 spent heavy weight on classic Cognitive Services SDK plumbing (custom vision projects, LUIS/CLU intents, Form Recognizer models). AI-103 reframes those as Foundry Tools and folds them under generative pipelines: vision becomes image and video generation plus multimodal understanding, document handling becomes Content Understanding , and language tasks are solved with generative prompting first.
- AI-102 emphasis AI-103 equivalent Practical change
- Cognitive Services resource + key Foundry project + managed identity Keyless auth is the default expectation
Custom Vision classifier training Multimodal model + Content Understanding Less model-training, more prompting and grounding
LUIS/CLU intent design Generative entity and intent extraction Prompt-and-extract replaces utterance labeling
Form Recognizer custom models Document Intelligence + Content Understanding OCR plus layout feeds RAG, not just a typed result
Bot/QnA flows Foundry agents with tools and memory Agents orchestrate retrieval, function-calling, and approval
If your background is AI-102, do not assume your service-name knowledge transfers cleanly. The biggest exam risk is reaching for a retired pattern (raw keys, public endpoints, custom-trained classifiers) when the scenario rewards a governed Foundry design.
What This Means for Study Planning
Spend the first review block on Foundry structure, identity, networking, deployments, content safety, monitoring, and evaluation.
Do not separate agents from operations; the blueprint expects agent behavior to be governed, traced, evaluated, and constrained.
Expect case studies and performance-based items , not just multiple choice. Microsoft uses multiple-response, drag-and-drop ordering, and scenario sets that test design judgment under constraints.
The exam is Python-oriented : the audience profile assumes app development experience in Python plus familiarity with general AI, generative AI, and Azure services.
Use local practice questions for coverage signals, but do not memorize wording or treat them as official exam items; pair them with the official Practice Assessment on AI Skills Navigator.
Keep a source checklist: the Microsoft Learn AI-103 study guide, the certification page, Foundry docs, Content Safety docs, and the local metadata files.
Renewal: Once earned, Microsoft associate-level credentials expire annually and are renewed for free through an online assessment on Microsoft Learn during the six-month window before expiration. There is no re-exam fee to renew, and the renewal assessment is unproctored.
The safest final-week target is not just answering service-name questions. You should be able to explain why a team would choose a Foundry project, a hub-based project, a model deployment option, a private endpoint, a managed identity, or a continuous-evaluation signal in a realistic production scenario.