Azure DP-100 glossary of data prep, training, deployment, and machine learning operations terms.
Use this glossary when Microsoft Certified: Azure Data Scientist Associate (DP-100) terms start to blur together. The goal is practical recognition, not encyclopedia coverage.
| Term | Exam meaning |
|---|---|
| MLOps | Operational practices for training, deploying, monitoring, and governing machine learning models. |
| GenAIOps | Operational practices for generative AI apps, including prompts, retrieval, tools, safety, and evaluation. |
| Model registry | A controlled place to version, approve, and deploy models. |
| Drift | Change in data or behavior that can degrade model performance after deployment. |
| Pipeline | Repeatable workflow for data preparation, training, evaluation, and deployment. |
| IaC | Infrastructure as code: provisioning environments with repeatable definitions rather than manual clicks. |
| Pair | How to separate them |
|---|---|
| MLOps infrastructure vs Model lifecycle | Ask which layer the scenario is testing, then match the answer to that layer only. |
| Control vs evidence | A control changes behavior; evidence proves behavior or supports investigation. |
| Managed service vs custom build | Managed services win for lower operational effort unless the requirement needs unsupported customization. |
| Prevention vs detection | Prevention blocks or reduces a bad event; detection finds or reports that it happened. |
Do not memorize terms in isolation. For each term, write one scenario where it is the best answer, one scenario where it is a distractor, and one signal that proves it worked.