Study Azure AI-900 GenAI Scenarios: key concepts, common traps, and exam decision cues.
Generative-AI questions become manageable when you ask one thing first: does the task actually require new content? If the answer is no, a narrower AI capability is often better. If the answer is yes, AI-900 then wants you to think about grounding, output quality, and responsible use.
Grounding: Providing trusted context so a model answers from relevant material instead of only from its general pretrained knowledge.
| Requirement | Strongest first fit |
|---|---|
| draft an email or report | generative AI |
| summarize a long conversation or document | generative AI |
| answer in a conversational style | generative AI |
| extract fields from a receipt | document processing, not generative AI |
| assign a support ticket category | classification, not generative AI |
Without grounding, a model may answer from general prior knowledge and produce unsupported output. With grounding, the model can rely on trusted source material. AI-900 expects you to recognize grounding as the stronger pattern when enterprise answers must stay close to internal content.
| Concern | Why it matters |
|---|---|
| hallucination | the output may sound confident but be unsupported |
| harmful content | generated responses can create safety and trust risks |
| privacy and security | prompts and retrieved content may contain sensitive data |
| transparency | users should understand that AI generated the response |
| accountability | humans still need ownership for risky outputs |
| Trap | Better rule |
|---|---|
| choosing generative AI for any text-related question | many text tasks are analysis, extraction, or translation instead |
| assuming grounding is the same as model retraining | grounding supplies runtime context; it does not change the model’s learned parameters |
| treating responsible AI as a separate topic from generation | generative systems create their own reliability, safety, and transparency risks |