Review the Microsoft Azure AI Apps and Agents Developer Associate (AI-103) scope, Azure AI decision points, common traps, and practice strategy before using IT Mastery.
Use this cheat sheet to tighten the decisions AI-103 usually turns into scenario questions: when to use Microsoft Foundry, how to ground generative AI answers, where agent tools fit, and how to keep Azure AI solutions observable and controlled.
Use this with practice. Review the distinctions below, then try the free diagnostic or open the full AI-103 route in IT Mastery.
| Field | Detail |
|---|---|
| Issuer | Microsoft |
| Certification lane | Microsoft Certified: Azure AI Apps and Agents Developer Associate |
| Exam code | AI-103 |
| Official exam name | Developing AI Apps and Agents on Azure |
| Passing score | Microsoft Learn study-guide resources list 700 or greater |
| IT Mastery status | Live AI-103 practice available |
| Domain | Weight | What to know | Common trap |
|---|---|---|---|
| Plan and manage an Azure AI solution | 25-30% | Projects, deployments, identity, evaluation, monitoring, governance, and lifecycle decisions | Jumping into model prompts before choosing the right platform boundary |
| Implement generative AI and agentic solutions | 30-35% | Grounding, retrieval, tool use, prompt workflows, content safety, and agent orchestration | Treating every generative AI scenario as a plain chat-completion problem |
| Implement computer vision solutions | 10-15% | Image analysis, OCR, multimodal inputs, and vision service fit | Confusing document extraction, OCR, and image understanding |
| Implement text analysis solutions | 10-15% | Language detection, key phrases, entities, sentiment, summarization, and translation | Choosing generative AI when a deterministic language service is enough |
| Implement information extraction solutions | 10-15% | Document intelligence, forms, fields, tables, validation, and downstream workflows | Ignoring confidence, human review, or document layout requirements |
| Distinction | How to decide |
|---|---|
| Foundry project vs app code | Use the project for model, deployment, evaluation, and governance configuration; use app code for product-specific workflow and integration. |
| Prompt-only vs grounded answer | Ground when answers must use private, current, or cited enterprise content. |
| Retrieval vs tool call | Retrieval brings knowledge into context; tool calls execute actions or query external systems. |
| Agent vs simple chat | Use an agent when the scenario needs multi-step reasoning, tools, instructions, or managed context. |
| Content safety vs evaluation | Content safety blocks or moderates unsafe output; evaluation measures quality, grounding, and reliability. |
| OCR vs document intelligence | OCR reads text; document intelligence extracts structured fields and layout-aware values. |
| Entity extraction vs summarization | Extraction identifies structured facts; summarization compresses source content. |
| Managed identity vs key-based access | Prefer managed identity when Azure resources need secure service-to-service access without stored secrets. |
Start with the free AI-103 diagnostic to see which domain creates the most misses. If generative AI and agent questions dominate the misses, drill grounding, tools, evaluation, and safety before returning to mixed practice. If service-selection questions dominate, work through the vision, text analysis, and information-extraction topic pages until the input/output pattern is automatic.
When you are scoring consistently above your target on unseen mixed attempts and can explain why the best answer fits the service boundary, security requirement, and operational constraint, move from topic drills to timed mocks.