The AI-103 skills measured page should reflect Microsoft objective language, not local question counts. Use the four official skill areas to organize study and then map each area to the Azure service decisions it tests.
This skill area focuses on selecting Azure AI services, planning resources, managing access, applying responsible AI controls, evaluating systems, and choosing the right Foundry services for generative AI and agents. It is the foundation for the implementation topics that follow.
This area covers building generative applications and agentic workflows with Azure AI Foundry and Azure OpenAI. Study system messages, prompt design, model selection, tool definitions, connectors, grounding, evaluation, and operational considerations for generative systems.
Computer vision topics involve image and video workloads. Review when Azure AI Vision fits object detection, image analysis, multimodal processing, or video-related tasks, and keep those use cases separate from document extraction or text-based retrieval scenarios.
Information extraction focuses on retrieval, grounding, document extraction, structured outputs, and workflows that turn unstructured or semi-structured content into usable data. Azure AI Document Intelligence and Azure AI Search often appear here, but they solve different parts of the problem.
Use these live DotCreds study paths to keep moving without losing your place.
Official and vendor docs used to ground this page.
Documents Azure OpenAI Service content filtering, which appears in the source-backed concepts for this DotCreds bank.
Documents Azure OpenAI Service models, which appears in the source-backed concepts for this DotCreds bank.
Documents Vector search in Azure AI Search, which appears in the source-backed concepts for this DotCreds bank.
Flexible search understands AI-901, ai901, ai 901, 901, ai, network plus, and saa c03.