Opis:

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.

This exam is based on Red Hat OpenShift AI version 2.8 and Red Hat OpenShift Container Platform version 4.14.

Cel: 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®).

Grupa docelowa:

• 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.
• Red Hat Certified Engineers who wish to become a Red Hat Certified Architect (RHCA®)

Uwagi: Duration – 3 hours

Preparation
Red Hat encourages you to consider taking the course Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) to help prepare. Attendance in these classes is not required; students can choose to take just the exam.

While attending Red Hat classes can be an important part of your preparation, attending class does not guarantee success on the exam. Previous experience, practice, and native aptitude are also important determinants of success.

Many books and other resources on system administration for Red Hat products are available. Red Hat does not endorse any of these materials as preparation guides for exams. Nevertheless, you may find additional reading helpful to deepen your understanding.

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.

Recommended Training Tab

• Red Hat OpenShift AI Administration (AI263)
• Creating Machine Learning Models with Red Hat OpenShift AI (AI264)
• Deploying Machine Learning Models with Red Hat OpenShift AI (AI265)
• Automating AI/ML workflows with Red Hat OpenShift AI (AI266)

Note that the above courses can be taken as a bundle by taking: Developing and Deploying AI/ML applications on Red Hat OpenShift AI (AI267)

Wymagania:

Candidates for this exam should:

• Have taken Red Hat OpenShift Administration I: Containers & Kubernetes (DO180) course or have comparable work experience using OpenShift Container Platform
• Have taken Red Hat OpenShift Administration II: Operating a Production Kubernetes Cluster (DO280) 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
• Take our free assessment to find the course that best supports your preparation for this exam

Konspekt: Study points for the exam

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.

1. Install Red Hat OpenShift AI (RHOAI)
2. 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
3. Work with data science projects
   • Create, modify, and delete data science projects
   • Manage data science project permissions
4. 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
5. Use Git to manage Jupyter notebooks collaboratively
   • Upload an existing notebook from a Git repository
   • Push updated notebooks to a Git repository
6. 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
7. 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
8. Create data science pipelines
   • Create pipelines with Elyra
   • Create pipelines with Kubeflow

Notyfikacja: Note: The course outline is subject to change as technology advances and the underlying job evolves. For questions or confirmation on a specific objective or topic, please contact us at osec@osec.pl