NCP-AIO Workload Management Practice Question
A research group submits a distributed PyTorch training job spanning eight GPUs across two nodes. The job completes but produces a model with accuracy far below the single-node baseline, and logs show that several ranks started training before their peers had initialized the process group. The administrator must ensure that all ranks are launched together and that a failed rank terminates the whole job. Which combination of Kubernetes mechanisms should be used?
⚠ Common exam trap
The trap here is treating a plain Kubernetes Job with high parallelism as equivalent to gang scheduling, when it actually offers no atomic admission or rendezvous coordination across 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
✓
Use a gang-scheduling mechanism such as Volcano or the Kubeflow Training Operator with PyTorchJob and configure the rendezvous endpoint so all worker replicas are admitted together.
Distributed training needs collective admission and coordinated rank metadata. A gang scheduler plus the Kubeflow Training Operator's PyTorchJob admits all replicas together and injects rendezvous details, eliminating the race where early ranks initialize before their peers, while the controller fails the whole job if any replica cannot run.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule the job on a single node with eight GPUs and set the CUDA_VISIBLE_DEVICES variable per container to isolate each rank.
Why it's wrong here
Consolidating onto one node avoids cross-node timing differences but does not guarantee simultaneous process-group initialization, and it abandons the two-node topology the job requires. Setting CUDA_VISIBLE_DEVICES only restricts which devices a container sees; it neither coordinates ranks nor terminates the job when one rank dies.
- ✗
Create eight separate Deployments, one per rank, and pass the rank index through an environment variable in each Deployment manifest.
Why it's wrong here
Deployments are designed for long-running stateless replicas and offer no collective admission or rank coordination. Eight independent Deployments would start whenever their images are ready, so ranks could still join late, and a crash in one Deployment would be restarted in isolation instead of failing the entire training group.
- ✗
Deploy the job as a Kubernetes Job with parallelism set to eight and completions set to one, relying on the default pod startup ordering.
Why it's wrong here
A standard Job with parallelism controls how many pods run concurrently but provides no gang semantics or shared rendezvous information such as master address and rank. Pods start independently, so some ranks can begin before others, which reproduces the initialization race and will not reliably abort the job when a single rank fails.
- ✓
Use a gang-scheduling mechanism such as Volcano or the Kubeflow Training Operator with PyTorchJob and configure the rendezvous endpoint so all worker replicas are admitted together.
Why this is correct
Gang scheduling admits all replicas atomically, so no rank starts until every peer is schedulable, and the PyTorchJob controller injects the master address, rank, and world size into each pod while restarting or failing the group consistently. This directly fixes the initialization race and enforces all-or-nothing execution.
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JA
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
This NCP-AIO practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-AIO exam.