[Feb-2026 Newly Released] Pass NCP-AIO Exam – Real Questions & Answers [Q24-Q39] – IT Certification Exam Questions & Answers

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[Feb-2026 Newly Released] Pass NCP-AIO Exam – Real Questions and Answers

Pass NCP-AIO Review Guide, Reliable NCP-AIO Test Engine

QUESTION 24
A Slurm user needs to submit a batch job script for execution tomorrow.
Which command should be used to complete this task?

sbatch -begin=tomorrow

submit -begin=tomorrow

salloc -begin=tomorrow

srun -begin=tomorrow

QUESTION 25
What are the key considerations when selecting a storage solution for an AI data center that requires both high performance and scalability?

Only consider capacity; performance can be optimized later.

Focus solely on the lowest possible cost per terabyte.

Consider the balance of performance (IOPS, throughput), capacity, scalability, and cost.

Always choose the newest technology, regardless of cost or suitability.

Ignore the long-term data growth projections.

QUESTION 26
You have successfully pulled a TensorFlow container from NGC and now need to run it on your stand-alone GPU-enabled server.
Which command should you use to ensure that the container has access to all available GPUs?

kubectl create pod –gpu=all nvcr.io/nvidia/tensorflow:<tag>

docker run nvcr.io/nvidia/tensorflow:<tag>

docker start nvcr.io/nvidia/tensorflow:<tag>

docker run –gpus all nvcr.io/nvidia/tensorflow:<tag>

QUESTION 27
You are configuring BCM for cluster provisioning. You want to automate the installation of specific software packages on each newly provisioned node. How can you achieve this?

Include the package installation commands directly in the OS image.

Use a BCM post-provisioning script to install the packages.

Create a Kubernetes Job that runs on each node to install the packages.

Leverage a configuration management tool like Ansible or Chef within a BCM post-provisioning script.

Specify the packages in the ‘cluster.yamr file under the ‘packages’ section.

QUESTION 28
You are monitoring the resource utilization of a DGX SuperPOD cluster using NVIDIA Base Command Manager (BCM). The system is experiencing slow performance, and you need to identify the cause.
What is the most effective way to monitor GPU usage across nodes?

Check the job logs in Slurm for any errors related to resource requests.

Use the Base View dashboard to monitor GPU, CPU, and memory utilization in real-time.

Run the top command on each node to check CPU and memory usage.

Use nvidia-smi on each node to monitor GPU utilization manually.

QUESTION 29
A Docker container running a CUDA application terminates unexpectedly with an ‘out of memory’ error, despite the host machine having sufficient RAM. What are the potential causes and how would you diagnose them?

The container’s memory limit is too low. Increase the container’s memory limit using the -memory’ flag with ‘docker rum.

The GPU is running out of memory. Monitor GPU memory usage using ‘nvidia-smr within the container to identify memory leaks or excessive memory allocation.

The application is experiencing a memory leak within the container. Use memory profiling tools within the container to identify and fix the memory leak.

The host machine is experiencing swap thrashing. Monitor swap usage on the host machine and consider increasing RAM or optimizing the application’s memory usage.

The Docker image is corrupted. Rebuild the Docker image from scratch.

QUESTION 30
You are deploying a VMI container on a cloud platform, and you need to set up automatic scaling based on the GPU utilization. Which of the following approaches is MOST appropriate for implementing this?

Use Kubernetes Horizontal Pod Autoscaler (HPA) based on CPU utilization.

Manually monitor GPU utilization and scale the number of containers using the cloud provider’s CLI.

Use Kubernetes Horizontal Pod Autoscaler (HPA) with a custom metric that monitors GPU utilization using the NVIDIA DCGM Exporter.

Configure the container’s application to automatically scale itself based on GPU utilization.

GPU Utilization cannot be used for Autoscaling.

QUESTION 31
Explanation:
You are running a Docker container that utilizes NVIDIA GPUs for deep learning inference. The application inside the container fails to detect the GPUs. You’ve verified that NVIDIA drivers are installed on the host. What is the MOST likely cause and how do you fix it?

The NVIDIA Container Toolkit is not installed or configured correctly on the host machine. Install and configure the toolkit following NVIDIA’s official documentation.

The Docker daemon is not configured to use the NVIDIA runtime. Modify the ‘/etc/docker/daemon.json’ file to include “‘default-runtime”: “nvidia”‘ and restart the Docker daemon.

The CUDA version in the container does not match the driver version on the host. Ensure the container uses a CUDA version compatible with the host driver.

The ‘”gpus all’ flag was not used when running the container. Re-run the container with ‘docker run -gpus all …w.

The host’s firewall is blocking communication between the container and the NVIDIA drivers. Disable the firewall or create an exception for the container network.

QUESTION 32
A system administrator needs to configure and manage multiple installations of NVIDIA hardware ranging from single DGX BasePOD to SuperPOD.
Which software stack should be used?

NetQ

Fleet Command

Magnum IO

Base Command Manager

QUESTION 33
What steps should an administrator take if they encounter errors related to RDMA (Remote Direct Memory Access) when using Magnum IO?

Increase the number of network interfaces on each node to handle more traffic concurrently without using RDMA.

Disable RDMA entirely and rely on TCP/IP for all network communications between nodes.

Check that RDMA is properly enabled and configured on both storage and compute nodes for efficient data transfers.

Reboot all compute nodes after every job completion to reset RDMA settings automatically.

QUESTION 34
Describe a scenario where using MIG (Multi-lnstance GPU) on an NVIDIAAI 00 GPU within a Kubernetes cluster would be most beneficial. Explain why MIG is advantageous in that specific use case.

Running a single, large deep learning training job that requires the full resources of the A100 GPU.

Hosting a large number of small, independent AI inference services, each with modest GPU requirements.

Executing a computationally intensive scientific simulation that benefits from high GPU memory bandwidth.

Running a virtual desktop infrastructure (VDI) environment where each user requires dedicated GPU resources.

Distributing a single large training job across multiple GPUs on different nodes.

QUESTION 35
You are deploying a containerized AI application from NGC on a cluster with multiple GPU nodes. You want to ensure that the application is distributed across multiple GPUs and nodes for maximum performance. What strategies can you employ to achieve this?

Use a single container with multi-GPU support and configure it to utilize all available GPUs on the cluster.

Deploy multiple replicas of the container, each configured to use a specific subset of GPUs on different nodes.

Utilize a distributed training framework like Horovod or DeepSpeed to distribute the workload across multiple GPUs and nodes.

Configure Kubernetes resource quotas to limit the number of GPUs available to each container.

Employ a message queue system like RabbitMQ or Kafka to distribute data to the containers running on different nodes.

QUESTION 36
You’ve deployed a container from NGC on a Kubernetes cluster, but the application is experiencing intermittent GPU errors. You suspect memory leaks within the container are causing the issue. What is the most effective method to diagnose this problem?

Monitor the container’s CPU usage using ‘kubectl top pod’.

Use ‘nvidia-smi’ within the container to monitor GPU memory usage over time.

Analyze the application’s logs for CUDA error messages related to memory allocation.

Restart the container regularly to clear potential memory leaks.

Use NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.

QUESTION 37
You’ve noticed consistently high GPU utilization but low overall throughput in your AI inference service. You suspect that a CUDA kernel is not efficiently utilizing the GPU’s resources. Which profiling tool would provide the MOST detailed insights into kernel-level performance?

nvidia-smi’

NVIDIA Nsight Systems

‘top’

‘vmstat’

DCGM

QUESTION 38
You are using an all-flash array (AFA) for your AI training dat
a. You observe that the storage utilization is very low, but you are still experiencing performance bottlenecks. What could be the potential reasons for this and how can you troubleshoot them?

The AFA is not configured correctly to handle the specific I/O patterns of your AI workload (e.g., random reads, large sequential writes). Check the AFA’s configuration settings for block size, caching policies, and prefetching.

The AFA is over-provisioned, and the internal garbage collection processes are interfering with I/O operations. Reduce the amount of provisioned space.

The network connection between the compute nodes and the AFA is the bottleneck. IJpgrade the network infrastructure or optimize the data transfer protocols.

The AFA’s internal controllers are overloaded, even though the overall storage utilization is low. Monitor the controller utilization and consider upgrading the AFA or distributing the workload across multiple AFAs.

The AFA’s warranty has expired. Renewing the warranty will magically fix the performance issues.

QUESTION 39
Which of the following Magnum IO components would be MOST beneficial for accelerating data loading in a deep learning training pipeline that reads data directly from NVMe drives?

NVSHMEM

GPUDirect RDMA

GPUDirect Storage

CUDA-Aware MPI

InfiniBand