EX267 Red Hat Certified Specialist in OpenShift AI Exam Info and Questions Sharing

Understand Red Hat EX267 Exam Info

The EX267 Red Hat Certified Specialist in OpenShift AI exam is designed for IT professionals and data scientists looking to validate their skills in deploying, configuring, and managing AI/ML workloads on Red Hat OpenShift AI (RHOAI). Successfully passing this exam earns candidates the Red Hat Certified Specialist in OpenShift AI credential, which also contributes towards the prestigious Red Hat Certified Architect (RHCA) certification.

Duration: 3 hours
Format: Performance-based, with hands-on tasks that must be completed in a live lab environment.

Master Red Hat EX267 Exam Objectives

This hands-on, performance-based EX267 exam evaluates your ability to use Red Hat OpenShift AI in real-world scenarios. You’ll be tasked with completing a series of practical exercises using the Red Hat OpenShift Container Platform and OpenShift AI components, mimicking tasks you’d perform in an enterprise environment.

Here are the key objectives tested in the EX267 exam:

Install Red Hat OpenShift AI (RHOAI): Candidates must know how to properly install the RHOAI operator and related components within a functioning OpenShift cluster.
Configure and Manage RHOAI: This includes setting up user access, managing project environments, and ensuring tools like JupyterHub are running and accessible.
Work with Data Science Projects: Candidates are expected to create and manage data science projects inside RHOAI.
Use Data Science Workbenches: You’ll need to demonstrate knowledge of launching and using environments like Jupyter notebooks for model development.
Use Git for Notebook Collaboration: Integrating Git repositories with notebook environments to collaborate and track changes is a core task.
Work with Machine Learning Models: From training to evaluation, candidates must show familiarity with managing ML models using OpenShift AI tooling.
Save and Load Models: You must know how to persist models and retrieve them for inference or retraining.
Create Data Science Pipelines: Building automated pipelines for model training, evaluation, and deployment using OpenShift pipelines is also part of the test.

Final Tips for Passing EX267 Exam

Practice in a lab environment that mimics a real OpenShift AI deployment.
Review Red Hat documentation and official training such as Red Hat OpenShift AI: Fundamentals (RHODS).
Focus on task efficiency – the exam is time-limited and rewards hands-on speed and precision.
Master key OpenShift CLI (oc) and Git commands, as these are vital for many tasks.
Know how to troubleshoot common issues with notebook servers and deployments.

By preparing thoroughly across these domains and practicing in a hands-on environment, you’ll greatly improve your chances of becoming a Red Hat Certified Specialist in OpenShift AI. This credential not only strengthens your Red Hat certification portfolio but also demonstrates your ability to support cutting-edge AI solutions in enterprise Kubernetes environments.