PDF (New 2025) Actual NVIDIA NCP-AIO Exam Questions [Q13-Q36]

PDF (New 2025) Actual NVIDIA NCP-AIO Exam Questions

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NEW QUESTION # 13
You are managing a cluster with multiple nodes connected via NVLink and NVSwitch. After a network outage, some of the NVLink connections are showing as 'degraded' in 'nvsm show links'. What steps should you take to attempt to restore the connections to their optimal state? (Select TWO correct answers)

• A. Reboot all nodes in the cluster simultaneously.
• B. Check physical NVLink cable connections for damage or looseness.
• C. Update the BIOS on all servers.
• D. Restart the 'nvsm' service on all nodes.
• E. Run 'nvsm repair linkS on the affected nodes.

Answer: B,D

Explanation:
Restarting the 'nvsm' service can help re-establish the connections. Checking the physical cable connections is crucial to ensure they are secure and undamaged. 'nvsm repair links' is not a valid command. Rebooting the entire cluster may be necessary in some situations, but it's a more disruptive step to take initially. A BIOS update is unlikely to solve the problem if it arose after a network outage.

NEW QUESTION # 14
Given an NVIDIAAIOO GPU with MIG enabled, you want to create a monitoring dashboard that displays the GPU utilization (GPU core %, memory utilization) for each MIG instance in real-time. Which tools and metrics should you prioritize to effectively build this dashboard?

• A. Utilize 'nvidia-smi' and parse its output to extract metrics. Then use 'PS to collect CPU utilization metrics and display them in a table.
• B. Use NVIDIA Nsight Systems to profile application performance and extract GPU metrics. Use Prometheus as visualization tool.
• C. Use standard system monitoring tools like 'top' and 'vmstat' and display their combined output.
• D. Parse the output of '/proc/meminfo' for GPU memory utilization data.
• E. Use DCGM (Data Center GPU Manager) to collect metrics like 'gpu_utilization' and 'memory_utilization' , and then use a visualization tool like Grafana to display the data.

Answer: E

Explanation:
DCGM is the most appropriate tool because it's designed for monitoring NVIDIA GPUs in data centers and provides the necessary granular metrics (GPU utilization, memory utilization) for individual MIG instances. Grafana is a popular visualization tool that can integrate with DCGM. Nsight Systems focuses on profiling application performance rather than real-time monitoring. Other options are either too limited or not specifically designed for GPU monitoring.

NEW QUESTION # 15
You are using BCM to manage a Kubernetes cluster with multiple GPU nodes. You need to enable GPU monitoring using Prometheus and the NVIDIA DCGM exporter. Outline the steps required to accomplish this. Choose the correct sequence:

• A. 0 1. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 2. Configure the NVIDIA DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
• B. 0 1. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 2. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
• C. 0 1. Deploy the NVIDIA DCGM exporter as a Deployment in your Kubernetes cluster. 2. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 3. Install Prometheus in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
• D. 0 1. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 2. Install Prometheus in your Kubernetes cluster. 3. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 4. Verify GPU metrics are available in Prometheus.
• E. 0 1. Install Prometheus in your Kubernetes cluster. 2. Deploy the NVIDIA DCGM exporter as a DaemonSet in your Kubernetes cluster. 3. Configure Prometheus to scrape metrics from the DCGM exporter endpoints. 4. Verify GPU metrics are available in Prometheus.

Answer: E

Explanation:
Prometheus must be installed first to enable metric collection. The DCGM exporter is then deployed as a DaemonSet (to ensure it runs on every node) and configured, enabling Prometheus to scrape the GPU metrics. Finally, the metrics availability is verified.

NEW QUESTION # 16
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?

• A. Use the Base View dashboard to monitor GPU, CPU, and memory utilization in real-time.
• B. Run the top command on each node to check CPU and memory usage.
• C. Check the job logs in Slurm for any errors related to resource requests.
• D. Use nvidia-smi on each node to monitor GPU utilization manually.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
TheBase View dashboardin NVIDIA Base Command Manager provides a centralized and real-time overview of GPU, CPU, and memory utilization across all nodes in the DGX SuperPOD cluster. This tool allows administrators to quickly identify bottlenecks and resource usage patterns efficiently, unlike manually checking logs or running commands node-by-node.

NEW QUESTION # 17
You have deployed a VMI container with Triton Inference Server on a cloud provider that supports MIG (Multi-lnstance GPU). You have a single A100 GPU and you want to partition it into two MIG instances to serve two different models concurrently, each requiring half of the GPU's resources. What steps are necessary to achieve this?

• A. No special configuration is needed; Triton automatically detects and utilizes MIG instances.
• B. Configure the cloud provider's instance settings to automatically partition the GPU into MIG instances.
• C. Bake different drivers in Triton Container to target different MIG instances
• D. MIG is not a supported feature in Triton
• E. Partition the AIOO GPU into two MIG instances using the 'nvidia-smi' command-line tool, then configure Triton to use each MIG instance separately by specifying the corresponding UUIDs in the model configuration files.

Answer: E

Explanation:
To utilize MIG with Triton, you need to first partition the GPU into MIG instances using 'nvidia-smi' , and then configure Triton to use each MIG instance separately. This involves specifying the correct UUIDs for each MIG instance in the model configuration files, allowing Triton to isolate and utilize each partition effectively.

NEW QUESTION # 18
You are the administrator of a Run.ai cluster with ACM enabled. You need to implement a chargeback mechanism to accurately track GPU usage and allocate costs to different research groups. What key pieces of information do you need to collect and what Run.ai and/or ACM features can help automate this process?

• A. GPU utilization per job, job duration, and associated research group. ACM and Run.ai provide APIs and dashboards for collecting this data, which can then be integrated with a billing system.
• B. CPU utilization per job. This is the primary factor in determining costs.
• C. Total number of jobs submitted by each group. Run.ai provides a summary of job submissions in the UI.
• D. Average job completion time. Use this to distribute the cost equally.
• E. Network bandwidth used by each job. This is the best indicator of resource consumption.

Answer: A

Explanation:
For accurate chargeback, you need GPU utilization per job, job duration (to quantify resource usage over time), and the associated research group to whom the cost should be allocated. ACM and Run.ai provide APIs and dashboards for collecting this data, which can be integrated with a billing system for automated chargeback. While the total number of jobs submitted can be an indicator of activity, it doesn't reflect actual resource usage. CPU utilization and network bandwidth are less relevant than GPU utilization in a GPU-accelerated environment. Average job completion time is insufficient for equitable cost allocation.

NEW QUESTION # 19
After completing the installation of a Kubernetes cluster on your NVIDIA DGX systems using BCM, how can you verify that all worker nodes are properly registered and ready?

• A. Check each node manually by logging in via SSH and verifying system status with systemctl.
• B. Run kubectl get pods to check if all worker pods are running as expected.
• C. Run kubectl get nodes to verify that all worker nodes show a status of "Ready".

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The standard method to verify that worker nodes are correctly registered and ready in a Kubernetes cluster is to runkubectl get nodes. This command lists all nodes and their statuses. Nodes showing a status of"Ready" indicates they are properly connected and available to schedule workloads. Checking pods or manual SSH is not the direct or reliable way to verify node readiness.

NEW QUESTION # 20
You need to do maintenance on a node. What should you do first?

• A. Disable job scheduling on all compute nodes in Slurm before completing maintenance.
• B. Set the node state to down in Slurm before completing maintenance.
• C. Set the node state to down in Slurm before completing maintenance.
• D. Drain the compute node using scontrol update.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Before performing maintenance on a compute node in Slurm, the best practice is todrain the nodeto prevent new jobs from being scheduled while allowing current jobs to finish. This is done using thescontrol update NodeName=<nodename> State=Draincommand or equivalent. Setting the node state to down immediately may disrupt running jobs, and disabling scheduling on all nodes is unnecessarily broad. Draining ensures a controlled transition for maintenance.

NEW QUESTION # 21
A data scientist submits a Run.ai job requesting 4 GPUs. However, due to resource constraints, only 2 GPUs are immediately available. You want the job to automatically start running as soon as the remaining 2 GPUs become available, without manual intervention. How do you configure Run.ai to achieve this?

• A. Use Run.ai's 'suspend' and 'resume' commands manually.
• B. Configure a lower priority for the job.
• C. Set the job's 'restartPolicy' to 'Always'.
• D. Set a higher quota for the team.
• E. Enable gang scheduling for the job.

Answer: E

Explanation:
Gang scheduling ensures that all requested resources (in this case, all 4 GPUs) are allocated before the job starts. The job will remain in a pending state until all resources are available, and then it will automatically start. 'restartPolicy only applies if a job fails after it has already started. Lower priority would make it less likely to start. Manually suspending and resuming requires intervention. A quota impacts how much you can submit overall, not the allocation of the complete resources requested by a single job.

NEW QUESTION # 22
A system administrator is looking to set up virtual machines in an HGX environment with NVIDIA Fabric Manager.
What three (3) tasks will Fabric Manager accomplish? (Choose three.)

• A. Coordinates with the GPU driver to initialize and train NVSwitch to GPU NVLink interconnects.
• B. Coordinates with the NVSwitch driver to train NVSwitch to NVSwitch NVLink interconnects.
• C. Installs vGPU driver as part of the Fabric Manager Package.
• D. Installs GPU operator
• E. Configures routing among NVSwitch ports.

Answer: A,B,E

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA Fabric Manager is responsible for managing the fabric interconnect in HGX systems, including:
* Configuring routing among NVSwitch ports (A)to optimize communication paths.
* Coordinating with the NVSwitch driver to train NVSwitch-to-NVSwitch NVLink interconnects (C)for high-speed link setup.
* Coordinating with the GPU driver to initialize and train NVSwitch-to-GPU NVLink interconnects (D) ensuring optimal connectivity between GPUs and switches.
Installing the GPU operator and vGPU driver is typically handled separately and not part of Fabric Manager's core tasks.

NEW QUESTION # 23
An AI model serving application is deployed on a multi-GPU server using Triton Inference Server. You notice that one GPU is consistently underutilized compared to the others. Which of the following could be contributing factors and how could you troubleshoot them?

• A. The server's CPU is underpowered.
• B. The load balancer distributing requests to Triton might not be evenly distributing the load across all GPUs. Examine the load balancer's configuration and metrics.
• C. One of the GPUs might be experiencing hardware issues. Use 'nvidia-smi' to monitor GPU health metrics like temperature and ECC errors.
• D. The NVIDIA driver version is outdated. Upgrade it.
• E. The model configuration in Triton might be pinning specific models to specific GPUs. Check the 'config.pbtxt file for 'instance_group' settings.

Answer: B,C,E

Explanation:
Triton allows pinning models to specific GPUs, so the configuration should be checked (A). Hardware issues (B) can cause underutilization, so GPU health should be monitored. An uneven load distribution from the load balancer (C) can also lead to underutilization of some GPUs. While an outdated driver or an underpowered CPU might impact overall performance, they are less likely to cause such a specific imbalance in GPU utilization.

NEW QUESTION # 24
What two (2) platforms should be used with Fabric Manager? (Choose two.)

• A. L40S Certified
• B. GeForce Series
• C. HGX
• D. DGX

Answer: C,D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA Fabric Manager is designed to manage and optimize fabric resources like NVLink and NVSwitch in enterprise-class platforms such as HGX and DGX systems. These platforms have the necessary hardware fabric components. The L40S Certified and GeForce series are either not compatible or do not require Fabric Manager.

NEW QUESTION # 25
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?

• A. Manually monitor GPU utilization and scale the number of containers using the cloud provider's CLI.
• B. GPU Utilization cannot be used for Autoscaling.
• C. Configure the container's application to automatically scale itself based on GPU utilization.
• D. Use Kubernetes Horizontal Pod Autoscaler (HPA) with a custom metric that monitors GPU utilization using the NVIDIA DCGM Exporter.
• E. Use Kubernetes Horizontal Pod Autoscaler (HPA) based on CPU utilization.

Answer: D

Explanation:
Using Kubernetes HPA with a custom metric based on GPU utilization is the most robust and automated approach. The NVIDIA DCGM Exporter provides GPU metrics that can be used by the HPA to trigger scaling events based on actual GPU usage. Option A will not consider GPU Utilization.

NEW QUESTION # 26
You are deploying a PyTorch container from NGC that utilizes Tensor Cores. How can you verify that Tensor Cores are being effectively used during inference?

• A. Use the NVIDIA Nsight Systems profiler to analyze GPU kernel execution and identify Tensor Core operations.
• B. Check the container logs for messages indicating Tensor Core usage.
• C. Analyze the training loss curve; a steep decline indicates Tensor Core usage.
• D. Use the 'nvidia-smi' command to monitor GPU utilization and check for high Tensor Core activity.
• E. Examine the CUDA code within the container to confirm explicit Tensor Core API calls.

Answer: A,D

Explanation:
B and E are correct. 'nvidia-smi' shows GPU utilization, including Tensor Core activity. Nsight Systems provides detailed profiling information, allowing you to identify specific Tensor Core operations. A is unreliable as log messages may not always be present. C refers to training, not inference. D is impractical without access to the container's source code.

NEW QUESTION # 27
You are deploying BCM in a high-availability (HA) configuration. What considerations are critical for ensuring data consistency and minimal downtime during a failover scenario?

• A. Use a highly available database cluster (e.g., PostgreSQL with replication) for the BCM database.
• B. Configure a load balancer to distribute traffic across multiple BCM instances.
• C. Implement a mechanism for automatically failing over the BCM service to a backup instance in case of a primary instance failure.
• D. Ensure that all BCM instances share a common storage volume for persistent data.
• E. Configure regular backups of the BCM database to a remote location.

Answer: A,B,C

Explanation:
In a HA configuration, a highly available database cluster is crucial for data consistency. A load balancer distributes traffic across multiple BCM instances, ensuring availability even if one instance fails. An automatic failover mechanism ensures minimal downtime by automatically switching to a backup instance. Sharing a common storage volume is generally not recommended due to potential data corruption issues. Regular backups are important but are more relevant for disaster recovery than immediate failover.

NEW QUESTION # 28
You're deploying a DOCA-based firewall application on a BlueField-2 DPU. The application uses eBPF for packet filtering. What is the primary reason for using eBPF in this scenario?

• A. To reduce CPU utilization on the host server by offloading packet filtering to the DPU.
• B. To automatically generate iptables rules on the host server.
• C. To simplify the firewall rule definition using a higher-level language.
• D. To enable dynamic updates to the firewall rules without requiring kernel module recompilation.
• E. To improve the compatibility with legacy network devices.

Answer: A,D

Explanation:
eBPF allows offloading packet filtering to the DPU, thus reducing the load on the host CPU. It also allows dynamic updates to firewall rules without requiring kernel recompilation, which is a significant advantage in terms of flexibility and maintenance.

NEW QUESTION # 29
A user complains that their AI training job is running very slowly. Upon investigation, you discover that the pod is scheduled onto a node with a slow network connection, causing significant delays in data transfer. How would you ensure that future similar jobs are scheduled onto nodes with faster network connections?

• A. Manually reschedule the pod onto a node with a faster network.
• B. Increase the resource requests for the pod to trigger rescheduling.
• C. Configure the kubelet to prioritize pods based on their network usage.
• D. Implement node affinity rules based on network bandwidth labels, and label the nodes appropriately.
• E. Use inter-pod affinity to force the job onto nodes already running network-intensive workloads.

Answer: D

Explanation:
The correct answer is B. By labeling nodes with their network bandwidth capabilities (e.g., 'network-bandwidth: 100GbpS), you can then use node affinity rules in your pod specifications to ensure that jobs requiring high bandwidth are scheduled onto suitable nodes. Option A is a temporary fix. Options C and D do not address the core issue of network bandwidth. Option E would exacerbate the problem by concentrating network-intensive workloads on the same nodes.

NEW QUESTION # 30
You are deploying an AI workload on a Kubernetes cluster that requires access to GPUs for training deep learning models. However, the pods are not able to detect the GPUs on the nodes.
What would be the first step to troubleshoot this issue?

• A. Verify that the NVIDIA GPU Operator is installed and running on the cluster.
• B. Check if the nodes have sufficient memory allocated for AI workloads.
• C. Ensure that all pods are using the latest version of TensorFlow or PyTorch.
• D. Increase the number of CPU cores allocated to each pod to ensure better resource utilization.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The first step in troubleshooting Kubernetes pods that cannot detect GPUs is to verify whether theNVIDIA GPU Operatoris properly installed and running. The GPU Operator manages the installation and configuration of all NVIDIA GPU components in the cluster, including drivers, device plugins, and monitoring tools. Without it, pods will not have access to GPU resources. Ensuring correct installation and operational status of the GPU Operator is essential before checking application-level versions or resource allocations.

NEW QUESTION # 31
A DGX H100 system in a cluster is showing performance issues when running jobs.
Which command should be run to generate system logs related to the health report?

• A. nvsm dump health
• B. nvsm health --dump-log
• C. nvsm get logs
• D. nvsm show logs --save

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
For troubleshooting and performance optimization on NVIDIA DGX systems such as DGX H100, the NVIDIA System Management (nvsm)tool is used to gather system health and diagnostic data. The command nvsm dump health is the correct command to generate and export detailed system logs related to the health report of the DGX system.
* nvsm show logs --save is not a recognized command format.
* nvsm get logs retrieves logs but does not specifically dump the health report logs.
* nvsm health --dump-log is not a standard documented nvsm command.
Therefore, nvsm dump health is the valid and documented command used to generate system logs focused on health reporting, useful for diagnosing performance issues in DGX H100 systems.
This usage aligns with NVIDIA's system management tools guidance for DGX platforms as described in NVIDIA AI Operations documentation for troubleshooting and performance optimization.

NEW QUESTION # 32
A system administrator of a high-performance computing (HPC) cluster that uses an InfiniBand fabric for high-speed interconnects between nodes received reports from researchers that they are experiencing unusually slow data transfer rates between two specific compute nodes. The system administrator needs to ensure the path between these two nodes is optimal.
What command should be used?

• A. ibstatus
• B. ibping
• C. ibnetdiscover
• D. ibtracert

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To verify the optimal communication path and diagnose issues between two nodes in an InfiniBand fabric, theibtracertcommand is used. It traces the route that InfiniBand packets take through the fabric, identifying each hop and any potential bottlenecks or faulty links along the path.
* ibstatusprovides status information about local InfiniBand devices and ports.
* ibpingtests connectivity and latency between nodes.
* ibnetdiscoverdiscovers and prints the topology of the InfiniBand fabric but does not trace specific paths.
Therefore,ibtracertis the appropriate tool for path optimization verification between two compute nodes.

NEW QUESTION # 33
You are trying to configure MIG (Multi-lnstance GPU) on your Run.ai cluster. You have an NVIDIAA100 GPU and want to create two MIG instances, each with 20GB of memory. Assuming the A100 has 80GB of memory, what is the CORRECT MIG profile string you would use when submitting a job to request one of these MIG instances?

• A. 1g.10gb
• B. 4g.20gb
• C. 1g.5gb
• D. 2g.10gb
• E. 2g.20gb

Answer: D

Explanation:
The MIG profile string follows the format 'GPU instances>g.gb'. In this case, '2g.10gb' is the correct MIG profile. This is because the A100 GPU will be split into 2 instances with 10 GB memory each, not 20GB as asked in the question. Even if the A100 has 80GB of memory, MIG is not a 1-1 memory division ratio.

NEW QUESTION # 34
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?

• A. 'vmstat'
• B. NVIDIA Nsight Systems
• C. 'top'
• D. nvidia-smi'
• E. DCGM

Answer: B

Explanation:
NVIDIA Nsight Systems (and its successor Nsight Compute for kernel-level analysis) is specifically designed for profiling CUDA kernels. It provides detailed information on kernel execution time, memory access patterns, and instruction-level performance, allowing you to identify inefficiencies. 'nvidia-smr and DCGM provide high-level GPU monitoring, while 'top' and 'vmstat' are system-level tools.

NEW QUESTION # 35
Which of the following statements correctly describe the function and purpose of the NVIDIA Container Toolkit?

• A. It is only required for running inference workloads, not training workloads.
• B. It patches the Linux kernel to enable GPU virtualization.
• C. It provides the necessary NVIDIA drivers and libraries inside the container for GPU access.
• D. It configures the Docker daemon or containerd to enable GPU passthrough into containers.
• E. It automatically scales the number of GPUs allocated to each container based on demand.

Answer: C,D

Explanation:
The correct answers are A and B. The NVIDIA Container Toolkit ensures that containers have access to the appropriate NVIDIA drivers and libraries, allowing them to leverage GPUs. It configures the container runtime (Docker or containerd) to correctly pass the GPU into the container environment. It's required for both training and inference. It doesn't automatically scale GPUs, nor does it patch the kernel.

NEW QUESTION # 36
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