Prepare for the Red Hat Certified Developer in AI exam by mastering the OpenShift AI knowledge behind every task
These practice tests are for developers, data scientists, and platform engineers preparing for the Red Hat Certified Developer in AI credential. This credential was previously called Red Hat Certified Specialist in OpenShift AI. The exam is performance-based: you complete real tasks on Red Hat OpenShift AI, from setting up workbenches and data connections to serving large language models and building generative AI applications. You can only do that quickly if you already know which component, setting, or workflow each task needs. This course builds that knowledge through scenario-based questions, so your lab practice and your exam time go further.
Why this course
On a hands-on exam, the biggest time sink is hesitation: not knowing which serving runtime fits, where a model should be stored, or how to route a workload to the right node. Each question here describes a realistic OpenShift AI situation and asks you for the correct approach. Examples include serving a large language model, comparing pipeline runs, tracking data drift, and adding guardrails to a chatbot. The questions are designed to support your hands-on practice, not replace it. All questions are original, aligned with the current exam objectives, and updated as OpenShift AI evolves.
What’s inside, by exam objective
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Architecture and fundamentals: how OpenShift AI relates to OpenShift Container Platform, MLOps and GenAIOps concepts, and how platform components work together in data science projects
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Projects, workbenches, and data connections: projects and permissions, custom workbench images and sizes, TensorBoard monitoring, and S3 and database connections
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Resource allocation: node selectors and tolerations, and placing workbenches and model servers on the right nodes
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Model serving and storage: KServe architecture, standard and advanced deployment modes, storing models in S3, OCI containers, or persistent volume claims, the OpenVINO and vLLM runtimes, and custom serving runtimes
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Model Registry: packaging models as OCI artifacts, then registering, versioning, deploying, and querying models
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Monitoring: bias and data drift detection with TrustyAI, hardware monitoring with Grafana, and optimizing resource use
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Data science pipelines: pipeline servers, building pipelines with Elyra and the Kubeflow SDK, artifacts, and experiments
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Model optimization and evaluation: choosing models from the catalog and Hugging Face, compressing and quantizing with LLM Compressor, and benchmarking with LMEval
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Generative AI applications: streaming responses, retrieval-augmented generation with vector databases, agentic tool use, and guardrails
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Development with Git: version-controlled notebooks, training models in Python, and saving and exporting models
How the explanations help you learn
Every question includes a detailed explanation for every answer option. You learn why the right approach works on OpenShift AI and why each alternative fails. An alternative might use the wrong runtime, an unsupported storage location, or a setting that blocks scheduling. Understanding these trade-offs helps you make the right call quickly when you’re working at the console.
Who it’s for
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Developers, data scientists, system administrators, and architects who build, serve, or run AI and ML workloads on Red Hat OpenShift AI
Start preparing today
You get lifetime access, regular updates, and the protection of Udemy’s 30-day refund policy. Enroll now, pair this course with hands-on lab practice, and go into prepared.
Disclaimer
This course is an independent, unofficial study resource. It is not affiliated with, endorsed by, or sponsored by Red Hat, Inc. Red Hat, OpenShift, and related names are trademarks of Red Hat, Inc. KServe, vLLM, Kubeflow, Elyra, TrustyAI, OpenVINO, Hugging Face, and Grafana belong to their respective owners. These names are used only to identify the exam and the technologies it covers. All questions are original practice material and are not drawn from any Red Hat exam. Because is a hands-on, performance-based exam, use this course together with practical lab work.
