Free NVIDIA NCP-AIO Actual Exam Questions

Makes sense to rule out A and C since they don’t directly address network speed or latency. B with jumbo frames can reduce overhead a bit but won’t fix latency issues much, especially for distributed training. D stands out because InfiniBand is designed for exactly this kind of high-speed, low-latency communication in HPC environments, which fits the problem perfectly. So, I’d go with D here as the best option to optimize inter-node communication for TensorFlow jobs.

Does B really help that much if the bottleneck is latency, not just packet size?

D imo. If delays happen during ETL, checking GPUDirect Storage setup seems key since it cuts down unnecessary data hops, speeding things up. A or C don’t really address the root cause here.

D. If there are delays in ETL with Magnum IO, checking GPUDirect Storage setup is key since it’s designed to speed up data transfer straight to GPU memory, cutting down on bottlenecks.

C imo, without admin rights in kubeconfig, automation won’t have necessary access.

It’s C, because without proper kubeconfig permissions, the CLI can’t automate tasks across nodes.

C, because --replicas is the correct flag for scaling resources in kubectl.

C sounds right since kubectl scale uses --replicas to set pod count. But I’m curious if the job’s parallelism field also needs adjusting to actually run 4 pods simultaneously?

Makes sense to pick C since DCGM covers all listed GPU stuff comprehensively.

Option C nails it since DCGM is built for detailed GPU health, policy control, and NVSwitch management all in one place, unlike nvidia-smi which is more basic.

C The question asks where to find the VMI, not specifically where to deploy it, so NGC as the official NVIDIA source seems the best fit here over cloud marketplaces.

It’s C because the NVIDIA NGC catalog is the official hub for all NVIDIA AI Enterprise images, including the VMI. Marketplaces might host it too, but NGC is where you get the verified image first.

Option A makes sense since Rapids might need separate setup after deployment.

Maybe A, since Rapids might not be installed automatically, just drivers and toolkits.

B, because tcpdump and nvidia-smi don’t cover storage, and htop is more general CPU/memory.

It’s B, since iostat directly reports disk IO stats, unlike the others.

Option A is definitely the straightforward choice here since it directly shows the state of each node at the cluster level. You don’t get that from checking pods because pods run on nodes but don’t confirm node registration itself. Option C could be useful if you suspect a specific node has issues, but it’s too manual and time-consuming for a general check. So, sticking with A makes the most sense just to quickly ensure all worker nodes are up and ready without extra steps.

A imo, it’s the standard way to confirm nodes are registered and ready.

D/C? While D covers priority and quotas well, C could help inference scale automatically during peaks, which might be useful alongside priority settings. Just relying on replicas or namespaces doesn’t guarantee prioritization.

D, since PriorityClasses help ensure inference pods get scheduled before training ones.

Maybe A, since scontrol shows detailed reasons if you check jobs individually.

A imo, since scontrol shows detailed pending job reasons directly.

C imo, Fabric Manager is the only option related to managing GPU interconnects like NVLink and NVSwitch, which are critical for high-performance AI training in an HGX setup. The rest don’t fit virtualization needs.

Probably C. Fabric Manager is mainly about managing the NVLink and NVSwitch fabric, ensuring those interconnects between GPUs are working well and properly configured. It doesn't upgrade memory or handle video encoding or rendering directly. For virtualizing AI/ML workloads, having good control over the inter-GPU links is crucial, and that’s where Fabric Manager fits in since it helps optimize communication paths.

Option D is important because each control-plane node needs a unique external IP to communicate properly and be reachable by other nodes and components. Without that, the cluster setup could fail or behave unpredictably. This step is often overlooked but critical before running kubeadm init, especially in multi-node setups.

Disabling swap (B) is definitely key, but another crucial step is making sure each control-plane node has a proper network setup so they can communicate correctly. Without proper IP configuration or connectivity, the initialization can run into errors. So while B is important, I think D also matters because external IPs help with node discovery and cluster communication during init. The load balancer (A) usually comes after at least one control-plane is up, and Docker (C) is needed but not as critical as ensuring the nodes don’t have swap enabled first.

It’s definitely A because sbatch is the only command designed specifically for submitting batch job scripts. The others—submit isn’t a Slurm command, and salloc and srun are for interactive allocation and running tasks immediately, not scheduling batch jobs. Even if the -begin=tomorrow flag varies in support, the question’s focus is on submitting a batch script for later execution, which sbatch handles. So D or C can be ruled out since they don’t submit batch scripts.

A definitely fits best since sbatch is the command used to submit batch jobs. The others aren’t really for submitting scripts—they’re more about running or allocating resources directly. Even if the -begin=tomorrow syntax isn’t perfect, the question is about submitting a batch job for later execution, so sbatch makes the most sense here.

A. Draining the node puts it into a state where it finishes running jobs but doesn’t accept new ones, which seems like the safest first step before any real maintenance. Setting the node down (B/C) usually forces the node offline immediately, potentially killing jobs, so that feels a bit harsh if you can avoid it. D sounds like overkill—disabling scheduling on all nodes just because one needs maintenance doesn’t seem right. Better to isolate the node first by draining it.

Probably A. Draining prevents new jobs from starting but lets current ones finish gracefully, which seems safer before doing any maintenance. Setting it down (B or C) might be too harsh right away.