NCP-AIO Workload Management Practice Question
A research team runs a multi-node distributed training job spanning eight GPU nodes. Jobs frequently begin execution before all worker pods are running, and the collective initialization hangs until the operator manually scales the job down and up. The administrator wants the scheduler to admit the job only when all of its pods can be placed together. Which mechanism should be used?
⚠ Common exam trap
The trap here is reaching for priority or anti-affinity as a fix for partial startup; those affect ordering and node distribution but never make pod admission atomic, so the collective can still begin with missing ranks.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure gang scheduling through a scheduler plugin so the job's pods are placed atomically only when the full group can be accommodated.
Collective initialization requires all ranks to be present before computation proceeds, so partial startup leads to hangs. Gang scheduling solves this by treating the job group as a single scheduling unit and admitting it only when every member fits. Scheduler plugins that implement coscheduling or gang semantics provide this all-or-nothing placement, which is why they are standard in large distributed training environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the kube-scheduler's default backoff period so pods that fail to schedule retry less aggressively and wait for peers to become ready.
Why it's wrong here
Backoff governs how often an unschedulable pod is retried; it does not coordinate placement across a group. Pods that do fit will still start immediately while others wait, preserving the partial-start problem. Longer backoff only delays retries for pods that cannot be placed, which can worsen the hang rather than ensure all members start together.
- ✓
Configure gang scheduling through a scheduler plugin so the job's pods are placed atomically only when the full group can be accommodated.
Why this is correct
Gang scheduling holds the entire job group until every member can be placed on available resources, then admits them together. This directly prevents the partial-start condition that stalls collective initialization. Using a scheduler plugin that supports gang or coscheduling semantics gives the atomic placement guarantee the team needs, eliminating the manual scale-down and scale-up workaround.
- ✗
Set a high PriorityClass on every worker pod so the scheduler treats the group as important and places members as soon as resources appear.
Why it's wrong here
Priority influences ordering and can trigger preemption, but it does not require all members of a group to be placed simultaneously. High-priority pods may still start one at a time as capacity frees, producing the same partial-start stall. Priority addresses urgency and eviction decisions rather than the atomic admission semantics needed for coordinated collective startup.
- ✗
Add a pod anti-affinity rule requiring each worker to run on a distinct node so that placement spreads evenly across the eight GPU nodes.
Why it's wrong here
Anti-affinity spreads pods across nodes but does not make their admission atomic. Some workers can still schedule while others remain Pending because no suitable node exists, so the collective can still initialize partially and hang. This constraint addresses topology distribution, not the all-or-nothing placement guarantee the scenario demands.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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