Red Hat Certified Developer in Red Hat AI Exam

What you'll learn

• Candidates for the Red Hat Certified Specialist in OpenShift AI should be able to accomplish the following tasks. Relevant product specific documentation will be provided but candidates should be prepared to perform these tasks without assistance.
• Understand Red Hat OpenShift AI architecture and fundamentals: 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 workbenches: 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 connections: Create connections (S3, database, etc.), Store and retrieve data and artifacts from external services
• Identify and allocate resources: Use nodeSelectors and tolerations, Allocate workbenches and model servers to specific nodes
• Deploy and serve models: 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 Registry: 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 performance: 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 pipelines: 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 models: Select models from RHOAI catalog and Hugging Face; Optimize models with LLM Compressor (compression and quantization); Evaluate LLM performance with LMEval using standard and custom benchmarks
• Build GenAI applications: 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 models: 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 Models: Deploy models using OpenShift AI interface (Standard and Advanced modes); Store models using S3 buckets, OCI containers, or persistent volume claims; Understand supported model storage locations; Configure model deployment settings

Prerequisites

• ›Candidates for this exam should:
• ›Have taken Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course or have comparable work experience using OpenShift Container Platform.
• ›Have taken Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) or have comparable work experience using the features of OpenShift AI.
• ›Review the Red Hat Certified Specialist in OpenShift AI exam (EX267) objectives

Who should attend

• ›System and Software Architects who need to demonstrate an understanding of the features and functionality of Red Hat OpenShift AI.
• ›System Administrators or developer s who need to demonstrate the ability to configure, support and maintain OpenShift AI.
• ›Data Scientists who need to demonstrate an understanding of using OpenShift AI to develop, train, serve, test, and monitor AI/ML models and applications.

Preparation

Exam format

Scores and reporting