DataRobot Certified Professional study guide
DataRobot Certified Professional · Work through your exam, one topic at a time.
The DataRobot Certified Professional credential is intended for AI leaders and practitioners who build, operate, and govern AI agents on the DataRobot platform. According to DataRobot, the platform is designed to help enterprises "build, operate and govern their agents" and spans Agentic AI, Generative AI, Predictive AI, AI Governance, and AI Observability. The DataRobot Certified Professional study guide has 5 sections and about 2,006 words.
01Agentic AI Platform
The Agentic AI Platform domain tests how you design, operate, and govern agents on DataRobot rather than how you prompt a single model. An agent is an LLM-driven component with instructions, tools, memory, and policies; an agentic workflow composes steps or specialized agents to complete a business task.
456 words, about 2 min02Generative AI
The Generative AI domain tests the full app lifecycle: selecting models, designing and versioning prompts, grounding with retrieval, evaluating quality, and adding guardrails. DataRobot questions often frame GenAI as an application problem, not a model benchmark: the unit of evaluation is the task, with a rubric, test set, and acceptance threshold.
374 words, about 2 min03Predictive AI
The Predictive AI domain tests the AutoML workflow end to end: framing the problem, preparing data, choosing validation, comparing models, and deploying. On DataRobot, expect terminology such as project target, Autopilot, Leaderboard, Blueprint, Feature Discovery, and Deployment.
375 words, about 2 min04AI Governance
The AI Governance domain tests how AI assets are controlled across their lifecycle: intake, risk tiering, documentation, approval, monitoring evidence, and retirement. DataRobot governance content centers on the AI Catalog and Model Registry, lineage, access control, approval workflows, and auditability.
392 words, about 2 min05AI Observability
The AI Observability domain tests whether you can detect, diagnose, and act on production degradation for predictive models, GenAI apps, and agents. DataRobot monitoring concepts include data drift, target drift, accuracy, service health, alerts, and challenger models.
409 words, about 2 min