Red Hat / OpenShift AI / EX267
Become a Red Hat Certified Developer in AI
Apply OpenShift application skills to the AI/ML lifecycle. Red Hat recommends OpenShift development experience before this practical exam covering projects, workbenches, data, model serving, pipelines, monitoring, optimization and GenAI applications.
Recommended foundation · not a formal prerequisite
Build on EX288
Use this checkpoint to identify gaps, or move directly to the role-specific material if you already have the skills.
Core learning
Follow the current official objectives
- 1.OpenShift AI architecture and projects
- 2.Workbenches, Git, data connections, and resources
- 3.Predictive and generative model serving
- 4.Model Registry and OCI artifacts
- 5.Pipelines, experiments, monitoring, bias, and drift
- 6.Model optimization, evaluation, RAG, agents, and guardrails
Hands-on consolidation
Perform the work in an isolated lab
In an isolated OpenShift AI environment, create a governed project and workbench, connect safe sample data, train and version a small model, deploy it with a supported runtime, build a pipeline, monitor resource use and model behavior, then create a small RAG workflow with input and output guardrails.
You can manage the complete model lifecycle on OpenShift AI, explain resource and security choices, diagnose a failed deployment or pipeline, and validate model behavior and safeguards.
Knowledge check · not an exam simulator
Find gaps with our EX267 test
75 original, source-backed questions; the first 7 are free. Red Hat's official exam is performance-based, so complete the practical lab as well.
Try our EX267 knowledge test →