Microsoft AI-102 Is Retired: Move Your Azure AI Skills to AI-103

Microsoft AI-102 retired on June 30, 2026. New candidates should not prepare for it as an active exam. Microsoft has moved the Azure AI engineer path toward Microsoft AI-103, Developing AI Apps and Agents on Azure. The productive transition is to keep the Azure AI skills that still matter while updating your preparation for agentic applications, retrieval, generative AI, orchestration, safety, and modern operational practices.

What transfers from AI-102

AI-102 built valuable foundations in Azure AI services, language, vision, search, document processing, conversational solutions, security, monitoring, and responsible AI. Those concepts do not disappear. They become part of a broader application architecture where models, tools, data, agents, retrieval, and safeguards work together. ExamSnap’s AI-102 to AI-103 transition guide maps the legacy skill base to the newer target.

AI-103 changes the center of gravity

The newer path expects candidates to build AI applications and agents rather than treating each cognitive capability as a separate endpoint. Study how an application chooses models, grounds responses in enterprise data, invokes tools, maintains context, applies safety controls, and exposes observable behavior.

That requires architecture judgment. Ask where data comes from, how it is indexed, what information may be sent to a model, which identity the application uses, what tools an agent may invoke, and how you prevent an untrusted prompt from turning into an unsafe action.

Retrieval and grounding deserve hands-on practice

Build a small retrieval-augmented application. Ingest documents, create a searchable index, retrieve relevant context, generate a response, and capture citations or source references. Then test weak queries, missing documents, conflicting information, and permission boundaries.

The goal is not only to make a response look fluent. It is to understand when the response is grounded, what evidence supports it, and how the system behaves when relevant context is absent.

For a testable scenario, build a small policy assistant with several invented employee-handbook documents, including a deliberately outdated policy and one document restricted to managers. Sign in as two users with different access rights. Verify which passages retrieval can return for each user before measuring the final answer. A convincing summary based on a document the caller was never allowed to read is a security failure, not a successful retrieval. Record source IDs, document versions, filters, and the decision to abstain when the authorized index has no reliable answer.

Agents introduce tool-use risk

An agent that can call tools can change real systems. Practice least-privilege tool access, explicit parameter validation, confirmation for high-impact actions, and clear logging. Separate read-only retrieval from state-changing operations. The more authority an agent receives, the more important governance and audit become.

Consider an expense-approval agent. It may extract a receipt amount, propose a category, and explain why the request appears consistent with policy. That is different from authorizing reimbursement. The write operation should enforce the authenticated caller’s permissions, transaction limits, current approval state, and an idempotency check outside the model. Test ambiguous requests, repeated submissions, and a receipt containing instructions that try to override the workflow. The expected result is a traceable proposal or denial, not a privileged tool action caused by persuasive text in an untrusted document.

Responsible AI becomes an engineering control

Review content safety, evaluation, privacy, data protection, prompt-injection risk, model limitations, and human oversight. Responsible AI is not a paragraph at the end of a design. It affects data selection, model choice, user experience, escalation, testing, and monitoring.

Monitoring should include model behavior

Traditional application telemetry still matters: latency, errors, dependencies, availability, and cost. AI applications also need quality and safety evaluation. Track retrieval relevance, groundedness, harmful outputs, tool failures, token or model usage, and regression after model or prompt changes.

Separate retrieval tests from grounded-answer tests. A low-quality response may come from the wrong document version, a correct passage truncated during chunking, missing permissions, or generation that ignores valid evidence; a single overall score cannot isolate those causes. Keep representative authorized and unauthorized queries, expected source identifiers, answer criteria, tool-call traces, and safety outcomes. After changing a search index, model, prompt, or tool, rerun the affected cases and compare quality, latency, and cost before promoting the change. This turns production observations into a repeatable engineering feedback loop.

Use the current guide as your exam contract

Microsoft’s active AI-103 materials should decide what you study for certification. A current Microsoft AI-103 preparation guide is a better exam-planning base than any retired AI-102 checklist.

A transition project for former AI-102 learners

Take an AI-102-style solution such as document extraction, search, or language analysis and rebuild it as a modern application. Add retrieval, a model or agent layer, identity, safety controls, telemetry, and an explicit evaluation set. Document what stayed the same and what changed.

AI-102 is now history, but the knowledge can be a strong foundation. The key is to stop studying the retired exam as a destination and use those skills to accelerate into the current AI-103 role.