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Red Hat Certified Specialist in OpenShift AI (EX267)

EDU Trainings s.r.o.

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This exam is part of the Developing and Deploying AI/ML Applications on Red Hat OpenShift AI with Exam (AI268) bundle. This bundle is available at 15% discount, now through September 30, 2024.
The Red Hat Certified Specialist in OpenShift AI exam tests candidates‘ ability to deploy OpenShift AI and configure it to build, deploy and manage machine learning models to support AI enabled applications.
By passing this exam, you become a Red Hat Certified Specialist in OpenShift AI that also counts towards earning a Red Hat Certified Architect (RHCA®).
This exam is based on Red Hat OpenShift AI version 2.8 and Red Hat OpenShift Container Platform version 4.14. Exam format
This exam is a performance-based evaluation of skills and knowledge required to configure and manage Red Hat OpenShift AI. Candidates perform routine configuration and administrative tasks using Red Hat OpenShift Container Platform and Red Hat OpenShift AI and are evaluated on whether they have met specific objective criteria. Performance-based testing means that candidates must perform tasks similar to what they perform on the job.
Scores and reporting
Official scores for exams come exclusively from Red Hat Certification Central. Red Hat does not authorize examiners or training partners to report results to candidates directly. Scores on the exam are usually reported within 3 U.S. business days.
Exam results are reported as total scores. Red Hat does not report performance on individual items, nor will it provide additional information upon request.

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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.

Install Red Hat OpenShift AI (RHOAI)
Configure and manage RHOAI

Manage user and group permissions and resources
Manage DataScienceCluster object
Create and publish custom notebook images
Import custom notebook images
Manage idle notebook culling
Customize default workbench and model server sizes


Work with data science projects

Create, modify, and delete data science projects
Manage data science project permissions


Use data science workbenches

Understand Jupyter ecosystem
Create, modify, and delete workbenches
Start and stop workbenches
Manage data connections
Manage Persistent Volume Claim objects
Inspect workbench resources


Use Git to manage Jupyter notebooks collaboratively

Upload an existing notebook from a Git repository
Push updated notebooks to a Git repository


Work with machine learning models

Understand basic machine learning concepts
Train models in Python using popular foundational libraries
Load data in a scalable way
Monitor and evaluate the training process


Save and load models

Save, export, and share models
Deploy models as Python applications
Create a custom runtime in KServe
Deploy a model using ModelMesh


Create data science pipelines

Create pipelines with Elyra
Create pipelines with Kubeflow

Cieľová skupina

System and Software Architects who need to demonstrate an understanding of the  features and functionality of Red Hat OpenShift AI.
System Administrators or developers 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.
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