Azure AI Engineer (AI-300) pattern recognition

Service selection by capability boundary and RAG architecture design recur throughout AI-300

Use-case-to-service matching is the dominant question structure: a business requirement followed by options across the Azure AI portfolio, where the correct answer depends on the capability category and the described input or output type. RAG architecture questions form a second cluster: Azure AI Search configuration, index design, chunking strategy, and the integration pattern between retrieval and generation appear in multiple scenario variants. Responsible AI questions test principle application and safety control configuration — content filtering, groundedness detection, and transparency requirements — across different deployment contexts. Model customization questions test the decision between prompt engineering, few-shot learning, fine-tuning, and retrieval augmentation, with the scenario providing labeled-data availability, latency tolerance, and customization requirements as the deciding signals.