Latest NVIDIA NCP-AIO Practice Test Questions, NVIDIA AI Operations Exam Dumps [Q25-Q47]

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Latest NVIDIA NCP-AIO Practice Test Questions, NVIDIA AI Operations Exam Dumps

May-2026 Pass NVIDIA NCP-AIO Exam in First Attempt Easily

NVIDIA NCP-AIO Exam Syllabus Topics:

Topic	Details
Topic 1	Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.
Topic 2	Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.
Topic 3	Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.
Topic 4	Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.

NO.25 Consider the following code snippet using NVSHMEM:
If this program hangs indefinitely after ‘nvshmem_barrier all()’ inside the ‘if (my_pe 0)’ block, what is the MOST likely cause?

Incorrect use of ‘MPl Barrier’.

Deadlock due to insufficient NVLink bandwidth.

PE 0 is waiting for other PEs to complete within the loop, but other PEs have already finished and finalized.

Memory corruption in ‘nvshmem_malloc’ .

Incorrect use of ‘CUDA VISIBLE DEVICES’

NO.26 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?

Run kubectl get nodes to verify that all worker nodes show a status of “Ready”.

Run kubectl get pods to check if all worker pods are running as expected.

Check each node manually by logging in via SSH and verifying system status with systemctl.

NO.27 Your Kubernetes cluster is running a mixture of AI training and inference workloads. You want to ensure that inference services have higher priority over training jobs during peak resource usage times.
How would you configure Kubernetes to prioritize inference workloads?

Increase the number of replicas for inference services so they always have more resources than training jobs.

Set up a separate namespace for inference services and limit resource usage in other namespaces.

Use Horizontal Pod Autoscaling (HPA) based on memory usage to scale up inference services during peak times.

Implement ResourceQuotas and PriorityClasses to assign higher priority and resource guarantees to inference workloads over training jobs.

NO.28 You want to monitor the GPU utilization of your BCM-managed cluster. Which tool would provide the most comprehensive real-time and historical GPU metrics?

nvidia-smi’ on each individual node.

BCM’s built-in monitoring dashboard.

Kubernetes Dashboard.

Prometheus with the NVIDIA DCGM exporter.

‘top’ command on each node.

NO.29 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

NO.30 A user submits a Slurm job script with the following options:

Assuming each node has 4 GPUs, how many GPU resources will be allocated to this job across the entire cluster?

NO.31 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

NO.32 You are deploying a VMI container using Kubernetes and want to ensure that your container is scheduled on a node with at least one NVIDIA GPU. Which Kubernetes feature is BEST suited for this requirement?

Resource Quotas

Node Affinity

Pod Disruption Budgets

Horizontal Pod Autoscaling

Taints and Tolerations

NO.33 You’re tasked with configuring Slurm to prioritize jobs submitted by a specific research group. Which Slurm feature provides the MOST direct way to implement this prioritization?

Using the ‘sinfo’ command to manually reorder pending jobs.

Setting a higher ‘nice’ value for jobs submitted by other groups.

Configuring Slurm’s Fairshare scheduling with appropriate shares assigned to the research group.

Manually editing the Slurm job queue database.

Disabling preemption.

NO.34 You’re trying to build a Docker image that includes NCCL for multi-GPU training. You’ve installed NCCL using ‘apt-get’ , but when you run the container, you get errors indicating that NCCL cannot find the GPUs. What’s the MOST likely problem?

The NCCL version is incompatible with the CUDA driver version. Verify compatibility and install a compatible version of NCCL.

You haven’t set the ‘NCCL_DEBUG’ environment variable to ‘INFO’ or a higher level for debugging. Set this variable to get more verbose NCCL output.

The Docker container is not configured to use the NVIDIA runtime, so NCCL cannot access the GPUs. Configure the Docker daemon correctly.

The network configuration within the container is preventing NCCL from communicating between GPUs. Ensure proper network setup and firewall rules.

You did not explicitly install CUDA development headers, which are necessary for NCCL to function properly. Add the ‘cuda-nvcc’ package to your ‘apt-get install’ command.

NO.35 You’re troubleshooting a pod that’s failing to schedule on a node in your BCM-managed cluster, despite the node having available GPU resources. What’s the most likely cause, and how would you diagnose it?

The node is tainted, preventing pods from being scheduled. Use ‘kubectl describe node to check for taints.

The pod’s resource requests exceed the node’s capacity. Use *kubectl describe pod to view resource requests and limits.

The pod is missing the necessary NVIDIA device plugin. Check the pod’s YAML for ‘nvidia.com/gpu’ resource requests.

The Kubernetes scheduler is misconfigured. Restart the ‘kube-scheduler* service on the master node.

The node is cordoned, preventing new pods from being scheduled. Use ‘kubectl get nodes’ to check node status.

NO.36 You are configuring cloudbursting for your on-premises cluster using BCM, and you plan to extend the cluster into both AWS and Azure.
What is a key requirement for enabling cloudbursting across multiple cloud providers?

You only need to configure credentials for one cloud provider, as BCM will automatically replicate them across other providers.

You need to set up a single set of credentials that works across both AWS and Azure for seamless integration.

You must configure separate credentials for each cloud provider in BCM to enable their use in the cluster extension process.

BCM automatically detects and configures credentials for all supported cloud providers without requiring admin input.

NO.37 You are troubleshooting a cluster with NVIDIA NVLink and NVSwitch. The fabric manager service (‘nvsm’) appears to be running, but the NVLink topology is not being discovered correctly. What is the FIRST step you should take to isolate the issue?

Immediately restart all GPUs in the system.

Check the ‘/var/log/nvsm/nvsm.log’ file for any error messages or warnings.

Reinstall the NVIDIA drivers.

Increase the logging level of ‘nvsm’ to DEBUG and restart the service.

Check the system’s hardware for physical damage.

NO.38 You are tasked with deploying a deep learning framework container from NVIDIA NGC on a stand-alone GPU-enabled server.
What must you complete before pulling the container? (Choose two.)

Install Docker and the NVIDIA Container Toolkit on the server.

Set up a Kubernetes cluster to manage the container.

Install TensorFlow or PyTorch manually on the server before pulling the container.

Generate an NGC API key and log in to the NGC container registry using docker login.

NO.39 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

NO.40 You are managing a Kubernetes cluster running GPU-accelerated AI workloads. You need to ensure that sensitive data, such as API keys and database credentials, used by these workloads is securely managed and accessible only to authorized pods. What are the best practices and Kubernetes features for achieving this?

Store the sensitive data directly in the container image.

Store the sensitive data in environment variables within the pod definition.

Use Kubernetes Secrets to store the sensitive data and mount them as files or environment variables in the pods.

Store the sensitive data in a centralized configuration management system (e.g., HashiCorp Vault) and use a sidecar container to retrieve the data and inject it into the pods.

Encrypt the sensitive data using a symmetric key and store the encrypted data in a ConfigMap.

NO.41 You are deploying an AI application using Fleet Command. You want to ensure that the application automatically restarts if it crashes on an edge device. How can you achieve this?

Manually monitor the application and restart it if it crashes.

Configure a systemd service or similar process manager on the edge device to automatically restart the application.

Use Fleet Command’s built-in health check and auto-restart features (if available and configured).

Disable the application’s crash reporting to prevent crashes.

Increase the memory allocated to the application to prevent crashes.

NO.42 You are encountering issues with a DOCA application failing to initialize on a BlueField-3 DPU. The error logs indicate a problem with allocating memory. Which of the following steps would be most effective in diagnosing the root cause?

Check the DPU’s memory usage using tools like ‘free -m’ or ‘top’ to identify potential memory exhaustion.

Examine the DOCA application code for potential memory leaks or incorrect memory allocation sizes.

Verify that the DOCA application is requesting memory from the appropriate memory domain (e.g., DOCA MD) and that sufficient memory is available in that domain.

Disable SELinux on the DPU to eliminate potential permission issues affecting memory allocation.

Reinstall the DOCA SDK on the DPU to ensure that all memory management libraries are correctly installed.

NO.43 You are managing a high availability (HA) cluster that hosts mission-critical applications. One of the nodes in the cluster has failed, but the application remains available to users.
What mechanism is responsible for ensuring that the workload continues to run without interruption?

Manual intervention by the system administrator to restart services.

The failover mechanism that automatically transfers workloads to a standby node.

Data replication between nodes to ensure data integrity.

Load balancing across all nodes in the cluster.

NO.44 You’re deploying a deep learning model training job to your Kubernetes cluster managed through BCM. This job requires exclusive access to two GPUs on a specific node with high memory bandwidth. How would you best configure your pod to achieve this?

Use a ‘nodeSelector’ to target the specific node and request ‘nvidia.com/gpu: 2 in the pod’s resource requests.

Use a ‘nodeAffinity’ with ‘requiredDuringSchedulinglgnoredDuringExecution’ to target the specific node and request ‘nvidia.com/gpu: 2 in the pod’s resource requests.

Use ‘kubectl patch node -p to make the node only schedulable for your pod, and then request ‘nvidia.com/gpu: 2’ in the pod’s resource requests.

Use a combination of ‘nodeselector’ and ‘tolerations’ to target the specific node, and request ‘nvidia.com/gpu: 2’ in the pod’s resource requests.

All of the above would work.

NO.45 You are using MIG (Multi-lnstance GPU) on an NVIDIAAIOO GPU to partition the GPU into smaller instances. One of the MIG instances is experiencing significantly lower performance compared to other instances running the same workload. What could be the potential reasons for this?

The MIG instance might have been configured with fewer compute resources (e.g., fewer SMS) than other instances. Use ‘nvidia-smi’ to verify the configuration of each MIG instance.

The workload running on that MIG instance might be experiencing memory contention with other processes on the system. Monitor memory usage with tools like ‘free -m’ and ‘nvidia-smi’.

The MIG instance might be sharing PCle bandwidth with another device on the same PCle root complex. Check the PCle topology of the server.

The NVIDIA drivers are not properly configured for MIG.

The operating system is not compatible with MIG.

NO.46 An AI data center is planning to use NVMe over Fabrics (NVMe-oF) for its storage infrastructure. What are the primary advantages of NVMe-oF compared to traditional storage protocols like iSCSI or Fibre Channel?

Lower latency and higher throughput for accessing NVMe SSDs over a network.

Simplified storage management and configuration.

Reduced cost due to the use of commodity hardware.

Improved data security through built-in encryption.

Native support for object storage interfaces.

NO.47 You are tasked with configuring MIG on an NVIDIAA100 GPU for a mixed AI/HPC workload. You need to create two instances: one for a deep learning training job (requiring high memory bandwidth) and another for a molecular dynamics simulation (requiring high compute throughput). Which is the MOST optimal MIG configuration to create based on these workload requirements?

Two instances of lg.5gb. This ensures balanced resource allocation.

One instance of 2g.10gb for deep learning and one instance of lg.5gb for molecular dynamics.

One instance of 3g.20gb for deep learning and one instance of 4g.20gb for molecular dynamics simulation. This configuration dedicates larger memory and compute resources to each task based on the workload.

Two instances of 7g.40gb. This provides maximum performance for both workloads.

One instance of 4g.20gb for deep learning and one instance of 3g.20gb for molecular dynamics.