EX267
Red Hat credentialCER0216Active
Red Hat Certified Developer in AI
Exam code: EX267Red Hat OpenShift AI / Red Hat OpenShift Container PlatformOpenShift Container Platform 4.20
Red Hat
Credential
Certified Developer
credential overview
About this credential
Tests the ability to deploy OpenShift AI and configure it to build, deploy, and manage machine learning models supporting AI-enabled applications.
Who this is for
System and software architects
system administrators or developers
data scientists working with OpenShift AI.
Assessment
Exam details
Active
Exam blueprint
Skills measured
Understand Red Hat OpenShift AI architecture and fundamentals3 topics
- Understand RHOAI's relationship with OpenShift Container Platform
- Understand MLOps, GenAIOps, and AI/ML concepts
- Know how RHOAI components work in data science projects
Manage data science projects and workbenches4 topics
- Create, configure, and manage projects and permissions
- Create and edit workbenches with custom images, versions, and sizes
- Build and import custom workbench images
- Monitor resource usage and training processes with TensorBoard
Configure data connections2 topics
- Create connections such as S3 and database connections
- Store and retrieve data and artifacts from external services
Identify and allocate resources2 topics
- Use nodeSelectors and tolerations
- Allocate workbenches and model servers to specific nodes
Deploy and serve models6 topics
- Understand model serving workflow and KServe architecture
- Deploy models using Standard and Advanced modes
- Store models in S3 buckets, OCI containers, or PVCs
- Serve predictive models with OpenVINO runtime
- Deploy and serve LLMs with vLLM runtime
- Create and configure custom serving runtimes
Manage models with the Model Registry4 topics
- Package models as OCI image artifacts
- Register and version models in the Model Registry
- Deploy models from the Model Registry
- Query the Model Registry API
Monitor AI models and performance3 topics
- Monitor model bias and data drift with TrustyAI
- Monitor hardware consumption with OpenShift monitoring stack and Grafana
- Analyze resource utilization and optimize based on monitoring insights
Create and manage data science pipelines4 topics
- Create pipeline servers and pipelines with Elyra and KubeFlow SDK
- Use container components and manage artifacts
- Configure Kubernetes features in pipelines
- Use experiments to compare pipeline runs
Optimize and evaluate models3 topics
- Select models from RHOAI catalog and Hugging Face
- Optimize models with LLM Compressor using compression and quantization
- Evaluate LLM performance with LMEval using standard and custom benchmarks
Build GenAI applications5 topics
- Understand and apply GenAI application patterns
- Build simple GenAI applications with streaming responses
- Build RAG applications with vector databases and document processing
- Build agentic applications with tools and multi-step reasoning
- Implement guardrails for content safety and input/output validation
Collaborate with Git and develop ML models3 topics
- Manage Jupyter notebooks with Git version control
- Train models in Python using foundational ML libraries
- Load data scalably and save/export models
Deploy and Store Models4 topics
- Deploy models using OpenShift AI interface in Standard and Advanced modes
- Store models using S3 buckets, OCI containers, or persistent volume claims
- Understand supported model storage locations
- Configure model deployment settings
Before you certify
Requirements and recommended experience
Training or equivalent experience
Take DO288 or have comparable work experience using OpenShift Container Platform.
Training or equivalent experience
Take AI267 or have comparable work experience using OpenShift AI features.
Exam preparation
Review EX267 objectives.
Preparation
Official learning resources
Learning resource
Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications
Learning resource
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI
Credential lifecycle
Status, validity and renewal
Credential lifecycle
Corporate certification enablement
Turn this pathway into a team capability plan
We can align the learning path, instructor support, practice environment and delivery schedule to your team’s roles and certification target.