ced4568andClaude Sonnet 5
60fbf0c385
Pre-Phase-1 platform work, approved and tested earlier but not yet committed: - Terminal banner: "RootNode Academy Lab — node: k8s-lab", upstream KubeKosh attribution left untouched in footer/README/LICENSE. - RootNode curriculum mode: scenarios/rootnode-path.json (Foundation -> Workloads -> Networking -> Configuration -> Troubleshooting -> Real Ops -> Capstone), built from existing scenario content grouped by category, no new scenarios invented. New GET /api/curriculum/path endpoint. - Student roster + per-student progress: instructor-roster, pick-your-name login (no password), students/environments-adjacent progress table migrated to a (student_id, scenario_id) composite key so practice progress and exam sessions are isolated per student instead of one global bucket. New /api/roster, /api/login endpoints. - scenarios/data/*.json: added isolation_type metadata (shared_namespace vs ephemeral_cluster) from an audit of which scenarios execute cluster-scoped or node-level commands. This was Tier-1-isolation groundwork that got superseded same-day by the full-ephemeral-per-student architecture (see docs/LAB_ARCHITECTURE.md) — the field is kept as descriptive metadata only, per the note now in scenarios/SCHEMA.md. Not wasted: it's an accurate audit of which scenarios touch cluster-scoped resources, useful independent of isolation model. - .gitignore added (node_modules/, frontend/dist/, *.db, /data/, .env) — none existed before; without it, npm installs during testing would have landed in a commit. Known limitation still open: no frontend UI for any of this yet (login screen, roster-add form, curriculum view) — API-only. No auth on the roster-add endpoint. Co-Authored-By: Claude Sonnet 5 <[email protected]>
46 lines
2.3 KiB
JSON
46 lines
2.3 KiB
JSON
{
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"id": "pod-node-assignment-mcq",
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"title": "Assigning Pods to Nodes",
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"category": "Scheduling",
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"isolation_type": "shared_namespace",
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"difficulty": "Medium",
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"type": "mcq",
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"weight": 3,
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"description": "## Assigning Pods to Nodes\n\nA data-processing Pod **must** run on a specific node named `worker-gpu` that has GPU resources. You want to ensure the Pod is **always** scheduled on that exact node — regardless of node labels.\n\nWhich field in the Pod spec achieves this most directly?",
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"options": [
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{
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"id": "a",
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"text": "`spec.nodeSelector: {kubernetes.io/hostname: worker-gpu}` — schedules on nodes matching the label"
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},
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{
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"id": "b",
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"text": "`spec.nodeName: worker-gpu` — directly assigns the Pod to the named node, bypassing the scheduler"
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},
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{
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"id": "c",
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"text": "`spec.affinity.nodeAffinity` with `requiredDuringSchedulingIgnoredDuringExecution` targeting the node name"
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},
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{
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"id": "d",
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"text": "`spec.tolerations` with a toleration matching the node's taint"
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}
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],
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"correct_option": "b",
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"explanation": "`spec.nodeName` is the most direct method — the Pod is **directly bound** to the named node, bypassing the Kubernetes scheduler entirely. The pod will only run on that node and will stay in `Pending` if that node is unavailable. `nodeSelector` requires the node to have a matching label (e.g., `kubernetes.io/hostname` is auto-assigned, so option A would also work, but is less direct). `nodeAffinity` is more flexible and preferred for production. `tolerations` allow pods to be scheduled on tainted nodes but do not restrict them to a specific node.",
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"hints": [
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{
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"title": "nodeName vs nodeSelector",
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"body": "nodeName bypasses the scheduler and pins the pod to a named node. nodeSelector uses labels for a more flexible approach. For production, prefer nodeAffinity.",
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"command": "kubectl explain pod.spec.nodeName"
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},
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{
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"title": "Get the node's auto-assigned hostname label",
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"body": "The kubernetes.io/hostname label is automatically set on every node and matches the node name.",
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"command": "kubectl get nodes --show-labels"
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}
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],
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"setup_commands": [],
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"default_namespace": "default",
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"teardown_commands": []
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}
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