[Dec-2025] Verified NVIDIA Exam Dumps with NCP-AIO Exam Study Guide [Q10-Q29]

Q10. You’re deploying an AI inference application using NVIDIA Triton Inference Server in a Kubernetes cluster. Which of the following storage options is MOST suitable for storing the trained models, considering scalability and access speed?

Network File System (NFS) share mounted directly to each pod.

HostPath volume on each node.

Kubernetes PersistentVolume backed by a cloud object storage service (e.g., AWS S3, Google Cloud Storage).

Kubernetes EmptyDir volume.

Local SSD drives directly attached to the Kubernetes nodes.

Q11. A data scientist is training a deep learning model and notices slower than expected training times. The data scientist alerts a system administrator to inspect the issue. The system administrator suspects the disk IO is the issue.
What command should be used?

tcpdump

iostat

nvidia-smi

htop

Q12. The ‘nvsm’ service is consistently crashing on one of your nodes. Analyzing the core dump reveals a segmentation fault related to memory access within the NVSwitch driver. What is the MOST appropriate course of action?

Increase the swap space on the node.

Report the issue to NVIDIA support with the core dump and relevant system information.

Disable the NVSwitch on the affected node.

Recompile the NVSwitch driver with debugging symbols.

Try different versions of CUDA.

Q13. You are designing a data center for AI workloads, and power density is a key concern. Which of the following cooling solutions would be most appropriate for a rack with a power density of 40kW?

Raised floor air conditioning with hot aisle/cold aisle containment.

Direct liquid cooling (DLC) to the GPUs.

Rear door heat exchangers (RDHx).

In-row cooling units (CRAH).

Evaporative cooling towers.

Q14. You are tasked with optimizing a BCM pipeline that processes video streams in real-time. The pipeline frequently misses frames, resulting in dropped video. What are the most effective strategies to reduce frame drops?

Reduce the resolution or frame rate of the input video streams.

Optimize the most computationally intensive stages of the pipeline using GPU acceleration (e.g., TensorRT).

Implement asynchronous processing to allow stages to run in parallel.

Increase the buffer size for video frames to accommodate processing delays.

All of the above.

Q15. A system administrator is experiencing issues with Docker containers failing to start due to volume mounting problems. They suspect the issue is related to incorrect file permissions on shared volumes between the host and containers.
How should the administrator troubleshoot this issue?

Use the docker logs command to review the logs for error messages related to volume mounting and permissions.

Reinstall Docker to reset all configurations and resolve potential volume mounting issues.

Disable all shared folders between the host and container to prevent volume mounting errors.

Reduce the size of the mounted volumes to avoid permission conflicts during container startup.

Q16. You are managing multiple edge AI deployments using NVIDIA Fleet Command. You need to ensure that each AI application running on the same GPU is isolated from others to prevent interference.
Which feature of Fleet Command should you use to achieve this?

Remote Console

Secure NFS support

Multi-Instance GPU (MIG) support

Over-the-air updates

Q17. Consider the following Kubernetes manifest snippet:

Specifies that the container requires one NVIDIA GPU.

Limits the container to using only 1024MiB of GPU memory.

Requests one CPU core for the container.

Sets the container’s memory limit to 2048MiB.

Allocates 1024milliCPlJ (0.1 Core) for the container

Q18. When deploying BCM using Helm, which Helm chart parameter controls the resource limits for the BCM pods (CPU and Memory)?

bcm.resources.limits’

‘resources.bcmLimits’

resources.limits’

‘bcmResources.limits’

‘deployment.resources.limits’

Q19. A BCM pipeline deployed using containers is failing to launch with a ‘CUDA driver version is insufficient for CUDA runtime version’ error.
What is the root cause and solution?

The host machine’s NVIDIA driver is older than the CUDA runtime version used in the container. Upgrade the host’s NVIDIA driver.

The container image has an incorrect CUDA runtime version. Rebuild the container image with a CUDA runtime version compatible with the host driver.

The ‘nvidia-container-runtime’ is not properly configured. Verify the configuration and restart the container runtime.

The container is not requesting GPU resources correctly. Ensure the container runtime is configured to allow GPU access.

A and B

Q20. You’re troubleshooting a performance issue in a distributed training job running on your Slurm cluster. You suspect network bottlenecks are contributing to the problem. Which Slurm command and option(s) would be MOST helpful in diagnosing network performance issues within the job’s allocated nodes?

sstat -j

scontrol show job

srun -mpi=pmi2 iperf3 -s

squeue -l

sdiag

Q21. A cloud engineer is looking to deploy a digital fingerprinting pipeline using NVIDIA Morpheus and the NVIDIA AI Enterprise Virtual Machine Image (VMI).
Where would the cloud engineer find the VMI?

Github and Dockerhub

Azure, Google, Amazon Marketplaces

NVIDIA NGC

Developer Forums

Q22. Which of the following correctly identifies the key components of a Kubernetes cluster and their roles?

The control plane consists of the kube-apiserver, etcd, kube-scheduler, and kube-controller-manager, while worker nodes run kubelet and kube-proxy.

Worker nodes manage the kube-apiserver and etcd, while the control plane handles all container runtimes.

The control plane is responsible for running all application containers, while worker nodes manage network traffic through etcd.

The control plane includes the kubelet and kube-proxy, and worker nodes are responsible for running etcd and the scheduler.

Q23. You’ve implemented GPUDirect Storage in your data pipeline, but are seeing inconsistent performance gains. Sometimes it’s significantly faster, other times it’s barely better than your previous setup. Which of the following factors could explain this inconsistent behavior? SELECT TWO.

The data being read is highly compressible.

The storage system is experiencing variable latency.

The CPU is still heavily involved in preprocessing the data after it’s loaded into GPU memory.

The PCIe bandwidth between the NIC and GPU is saturated by other processes.

The CUDA compute version is not compatible with the version of GPUDirect Storage being used.

Q24. A user reports that their Docker container, which utilizes a specific GPU, is consistently slower than expected when performing inference. You need to diagnose whether the GPU is being utilized effectively. Which of the following approaches are MOST effective?

Use ‘nvidia-smi’ inside the container to monitor GPU utilization, memory usage, and temperature during inference.

Use ‘docker stats’ to monitor the container’s CPU and memory usage. High CPU usage might indicate a CPU bottleneck.

Profile the application code using profiling tools like ‘nvprof or ‘NVIDIA Nsight Systems to identify performance bottlenecks on the GPU.

Monitor network I/O using tools like Siftop’ or ‘tcpdump’ to check for network-related bottlenecks.

Run ‘nvidia-smi on the host to see the CUDA version and driver details to check compatibility issues.

Q25. A team is running a large distributed training job across multiple nodes in your Run.ai cluster. They are experiencing significant performance degradation due to network latency between the nodes. What are the possible solutions you can implement with Run.ai and potentially ACM to mitigate this issue?

Enable RDMA (Remote Direct Memory Access) and ensure proper network configuration for low-latency communication.

Configure node affinity rules to ensure that all nodes participating in the distributed training job are located within the same rack or network segment.

Increase the number of GPUs requested per node to reduce inter-node communication.

Implement data parallelism instead of model parallelism.

Use Run.ai’s built-in network acceleration features.

Q26. You are tasked with deploying a DOCA service on an NVIDIA BlueField DPU in an air-gapped data center environment. The DPU has the required BlueField OS version (3.9.0 or higher) installed, and you have access to the necessary container image from NVIDIA’s NGC catalog. However, you need to ensure that the deployment process is successful without an internet connection.
Which of the following steps should you take to deploy the DOCA service on the DPU?

Install Docker on the DPU, pull the container directly from NGC, and run it using ‘docker run’ with appropriate environment variables.

Pull the container image from NGC using Docker and modify the YAML file before deployment.

Manually download the container image and YAML file beforehand, transfer them to the DPU, and deploy using Kubernetes with standalone Kubelet.

Use the host system’s Docker engine to pull the container image and deploy it on the DPU via SSH.

Q27. You want to upgrade the NVIDIA drivers on your Kubernetes nodes without disrupting the running AI workloads. What is the recommended approach to perform a rolling upgrade of the NVIDIA drivers?

Drain each node, upgrade the drivers, and then uncordon the node.

Simultaneously upgrade the drivers on all nodes.

Delete all pods on the node, upgrade the drivers, and then recreate the pods.

Upgrade the drivers on a single node and then propagate the changes to other nodes using a script.

Use a Kubernetes DaemonSet to manage the driver installation and updates, ensuring a rolling update strategy.

Q28. When configuring a DOCA application that utilizes shared memory between the host and the DPU, which DOCA service is typically employed to facilitate this communication, and what are its benefits?

DOCA Memory Domain (MD): Provides a unified memory space accessible by both the host and the DPU, simplifying data sharing and synchronization.

DOCA Comm Channel: Offers a low-latency communication channel for control messages but is not suitable for large data transfers.

DOCA DMA: Enables direct memory access between the host and the DPU without CPU intervention, resulting in high-bandwidth data transfer.

DOCA Flow: Manages network traffic flows but does not directly address shared memory communication.

DOCA Telemetry: Provides performance monitoring but does not facilitate shared memory usage.

Q29. You are using BCM for configuring an active-passive high availability (HA) cluster for a firewall system. To ensure seamless failover, what is one best practice related to session synchronization between the active and passive nodes?

Configure both nodes with different zone names to avoid conflicts during failover.

Use heartbeat network for session synchronization between active and passive nodes.

Ensure that both nodes use different firewall models for redundancy.

Set up manual synchronization procedures to transfer session data when needed.

[Dec-2025] Verified NVIDIA Exam Dumps with NCP-AIO Exam Study Guide [Q10-Q29]

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