Red Hat Certified Specialist in OpenShift AI 온라인 연습
최종 업데이트 시간: 2025년08월07일
당신은 온라인 연습 문제를 통해 RedHat EX267 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 EX267 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 300개의 시험 문제와 답을 포함하십시오.
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Explanation:
The oc get pvc command lists all Persistent Volume Claims in the namespace, including those linked to workbenches.
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The oc get notebook <name> -o yaml command outputs the complete YAML definition of the specified workbench for inspection.
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The openshift-ai namespace is the default for deploying workbenches and AI-related resources in OpenShift AI environments.
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Explanation:
The oc logs <pod-name> command displays the logs of the workbench pod, providing useful information for debugging and monitoring.
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The spec.volumeClaim field in a notebook configuration specifies the Persistent Volume Claim (PVC) used for storage.
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Scaling the notebook to zero replicas with oc scale notebook <name> --replicas=0 stops the workbench without removing its configuration.
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Creating a workbench typically deploys a Pod for running the notebook and a Service to provide network access to it.
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The oc delete notebook <name> command deletes the specified workbench and its associated resources, such as pods.
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The oc edit notebook <name> command allows updating the configuration of an existing workbench directly in the YAML definition.
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Creating a workbench requires specifying a container image and a Persistent Volume Claim (PVC) for persistent storage.
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The oc get notebooks command shows all Jupyter notebook instances (workbenches) running within the specified namespace.
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A Notebook resource in OpenShift AI represents a workbench, providing an environment for running Jupyter notebooks.
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Jupyter notebooks are saved as .ipynb files, which store code, outputs, and metadata in a JSON-based format for easy sharing.
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Stopping a Jupyter notebook server requires pressing Ctrl + C in the terminal where the server is running, gracefully shutting it down.
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Jupyter notebooks natively support Python and R kernels, widely used for data science, statistical analysis, and visualization tasks.