Current AI-103 Exam Facts

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 June 2026 the certification carries a (beta) label, which changes two practical things: beta exams are not scored immediately because Microsoft is gathering question-quality data, and the official Practice Assessment is not yet published (Microsoft typically releases one within about eight weeks of an exam leaving beta).

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.

Current Blueprint Weights

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.

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; the official Practice Assessment is not yet available for this beta exam.
• 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.