In preparation

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.

• 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

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.

• 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)