[Aug-2026] Updated NVIDIA-Certified Professional NCP-AIO Exam Questions BUNDLE PACK
Master The NVIDIA Content NCP-AIO EXAM DUMPS WITH GUARANTEED SUCCESS!
NEW QUESTION # 33
You are configuring MIG for a Kubernetes cluster. Which of the following statements regarding the use of MIG with Kubernetes are correct? (Select TWO)
- A. Kubernetes natively supports MIG without any additional configuration.
- B. The NVIDIA GPU Operator is required to enable MIG support in Kubernetes and to manage GPU resources efficiently.
- C. MIG is not supported in Kubernetes.
- D. MIG allows you to partition a single physical GPU into multiple virtual GPUs, enabling you to run multiple GPU-accelerated workloads in isolation within the Kubernetes cluster.
- E. Kubernetes cannot schedule pods on specific MIG instances; it only schedules on the physical GPU.
Answer: B,D
Explanation:
The NVIDIA GPU Operator is essential for managing NVIDIA GPUs, including MIG instances, within a Kubernetes cluster. MIG allows partitioning of GPUs, enabling multiple isolated workloads. Kubernetes does schedule pods on specific MIG instances with proper configuration. Native Kubernetes support isn't comprehensive without the operator. MIG is supported.
NEW QUESTION # 34
You are deploying a multi-tenant AI platform on Kubernetes, where different teams share the same cluster. Each team should only be able to access and utilize the GPUs allocated to their respective namespaces. How can you enforce this isolation?
- A. Implement network policies to isolate the network traffic of different namespaces, thereby preventing unauthorized GPU access.
- B. Utilize resource quotas to limit the GPU usage of each namespace, and rely on users to respect these limits.
- C. Leverage a custom admission controller to validate GPU requests and ensure they originate from authorized namespaces.
- D. Use Kubernetes RBAC (Role-Based Access Control) to restrict access to GPU resources based on namespaces.
- E. Configure the NVIDIA Device Plugin to only expose GPUs to pods within specific namespaces.
Answer: C,D
Explanation:
The correct answers are A and E. RBAC allows you to control who can create, modify, or delete GPU resources within each namespace. It defines what actions (verbs) are allowed on what resources. Combined with a custom admission controller to validate the GPU request, you can enforce the access control across the namespaces. Implementing an admission controller enforces policies and validates requests to access GPU resources originated from authorized namespaces. Option B doesn't exist. Option C, although helpful for network segmentation, does not directly control GPU access. Option D only limits usage, not access.
NEW QUESTION # 35
Which of the following are benefits of using NVIDIA Fleet Command (Select all that apply)?
- A. Automatic GPU driver updates for non-NVIDIA GPUs.
- B. Simplified AI model deployment and management at the edge.
- C. Centralized monitoring and management of edge devices.
- D. Automated over-the-air (OTA) updates for AI models and system software.
- E. Enhanced security and access control for AI deployments.
Answer: B,C,D,E
Explanation:
Fleet Command provides simplified deployment (A), centralized monitoring (B), OTA updates (C), and enhanced security (D). It does not provide driver updates for non-NVIDIA GPUs (E).
NEW QUESTION # 36
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?
- A. docker start nvcr.io/nvidia/tensorflow:<tag>
- B. docker run nvcr.io/nvidia/tensorflow:<tag>
- C. kubectl create pod --gpu=all nvcr.io/nvidia/tensorflow:<tag>
- D. docker run --gpus all nvcr.io/nvidia/tensorflow:<tag>
Answer: D
Explanation:
When running a GPU-enabled container directly on a server with Docker, the flag --gpus all is required to allow the container access to all GPUs on the host system. This ensures that the TensorFlow container can utilize GPU resources fully. The other options either do not specify GPU access correctly or are Kubernetes-specific commands.
NEW QUESTION # 37
What is the main purpose of using Multi-lnstance GPU (MIG) with NVIDIA GPUs in a Kubernetes cluster managed by BCM, and what challenges does it help to address?
- A. To increase GPU utilization and enable resource sharing in multi-tenant environments by partitioning a physical GPU into multiple isolated instances.
- B. To simplify the deployment and management of GPU drivers and NVIDIA Container Toolkit on Kubernetes nodes.
- C. To improve the overall performance of GPU-accelerated applications by allowing multiple containers to share a single GPU's memory and compute resources concurrently.
- D. To facilitate running CPU-bound workloads directly on GPUs to maximize resource utilization.
- E. To enable the execution of CUDA-unaware applications within Kubernetes by abstracting away the underlying GPU hardware.
Answer: A
Explanation:
MIG's primary goal is to improve GPU utilization and enable resource sharing in multi-tenant environments. It helps address challenges related to GPU oversubscription and resource isolation by partitioning a physical GPU into multiple isolated instances. MIG directly increases the total resource pool for GPU. Option A is not the use case as the objective is not about overall performance but about enabling more workloads.
NEW QUESTION # 38
You need to monitor the GPU utilization of pods in your Kubernetes cluster running AI workloads. You want to use Prometheus to collect these metrics. Which of the following approaches is most suitable and efficient for collecting GPU metrics within the Kubernetes environment?
- A. Use the Kubernetes metrics server to collect GPU utilization metrics directly from the kubelet on each node.
- B. Manually SSH into each node and run 'nvidia-smi' to record GPU utilization, then input the data into Prometheus.
- C. Install the 'nvidia-dcgm-exporter' as a DaemonSet to expose GPU metrics from each node. Configure Prometheus to scrape these endpoints.
- D. Run 'nvidia-smi' inside each pod and expose the output as a Prometheus metric using a custom script and the Prometheus client library.
- E. Install the 'gpu-exporter' as a Deployment. Configure Prometheus to scrape these endpoints.
Answer: C
Explanation:
The correct answer is A. The 'nvidia-dcgm-exporter' is designed to efficiently expose GPU metrics from each node as Prometheus endpoints. Running it as a DaemonSet ensures that it runs on every node with GPUs. Option B is inefficient and requires significant overhead. Option C, the Kubernetes metrics server, does not collect detailed GPU utilization metrics. Option D is a manual and impractical approach. Option E is not standard and relies on an unspecified 'gpu-exporter' , whereas 'nvidia-dcgm-exporter' is an official solution.
NEW QUESTION # 39
What two (2) platforms should be used with Fabric Manager? (Choose two.)
- A. DGX
- B. HGX
- C. GeForce Series
- D. L40S Certified
Answer: A,B
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 # 40
What is the primary purpose of assigning a provisioning role to a node in NVIDIA Base Command Manager (BCM)?
- A. To enable the node to monitor GPU utilization across the cluster
- B. To assign the node as a storage manager for certified storage
- C. To allow the node to manage software images and provision other nodes
- D. To configure the node as a container orchestration manager
Answer: C
Explanation:
In NVIDIA Base Command Manager (BCM), assigning the provisioning role to a node enables that node to manage software images and perform provisioning tasks for other nodes in the cluster. This role allows automated deployment and configuration of cluster nodes, ensuring consistency and simplifying large-scale management. It is not primarily responsible for container orchestration, GPU monitoring, or storage management.
NEW QUESTION # 41
You are designing a data center network to support distributed deep learning training across multiple servers. The training job uses NCCL (NVIDIA Collective Communications Library) for inter-GPU communication. Which of the following network configurations will maximize the performance of NCCL?
- A. A network using only TCP/IP without RDMA support.
- B. A VLAN-based network with no QOS (Quality of Service) configured.
- C. A single network switch connecting all servers, with each server connected via a single IOGbE link.
- D. A Clos network topology with non-blocking links between all servers, utilizing RoCEv2 or InfiniBand.
- E. A traditional three-tier network architecture with oversubscribed links at each layer.
Answer: D
Explanation:
NCCL benefits greatly from low-latency, high-bandwidth communication. A Clos network with non-blocking links, RoCEv2, or InfiniBand ensures that GPUs can communicate efficiently without bottlenecks. A single switch with limited bandwidth, a three-tier network with oversubscription, or lack of RDMA will significantly hinder NCCL performance. VLANs without QOS do not guarantee low latency.
NEW QUESTION # 42
You want to limit the GPU memory usage of a specific container within a Kubernetes pod running an AI inference service. How can you achieve this using NVIDIA tools and Kubernetes resources?
- A. Set the 'CUDA VISIBLE_DEVICES' environment variable to an empty string for that container.
- B. Utilize the NVIDIA MPS (Multi-Process Service) and configure memory limits for each process using MPS control commands.
- C. Configure the Kubernetes scheduler to only schedule pods with GPU memory limits on nodes with sufficient free GPU memory.
- D. Set resource limits for 'nvidia.com/gpu' in the pod's resource requests and limits.
- E. Use the 'nvidia-smi' command within the container to limit the GPU memory usage of the process.
Answer: B
Explanation:
The correct answer is C. NVIDIA MPS (Multi-Process Service) allows multiple processes to share a single GPU, and it provides mechanisms to control the memory usage of each process. By configuring MPS, you can limit the GPU memory available to a specific container. Option A disables GPU access entirely. Option B is not a reliable way to enforce memory limits. Option D only controls the number of GPUs, not the memory usage per container. Option E describes scheduling based on available memory, but doesn't enforce limits.
NEW QUESTION # 43
Consider the following Dockerfile snippet for a VMI container deployment:
- A. Set the working directory to /app
- B. Copy the requirements.txt file and install python packages.
- C. Run the command 'python app.py'
- D. Copy all files from the current directory to /app
- E. Install CUDA toolkit version 11.0
Answer: A,B,C,D
Explanation:
The snippet performs the following actions: Sets the working directory using WORKDIR, Copies files using COPY, and Runs a Python script using CMD. It installs the python requirments using requirements.txt file as well.
NEW QUESTION # 44
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?
- A. Load balancing across all nodes in the cluster.
- B. The failover mechanism that automatically transfers workloads to a standby node.
- C. Data replication between nodes to ensure data integrity.
- D. Manual intervention by the system administrator to restart services.
Answer: B
Explanation:
In an HA cluster, the failover mechanism is responsible for detecting node failures and automatically transferring workloads to a standby or redundant node to maintain service availability. This process ensures mission-critical applications continue running without interruption. Load balancing helps distribute traffic but does not handle node failures. Manual intervention is not ideal for HA, and data replication ensures data integrity but does not itself manage workload continuity.
NEW QUESTION # 45
You need to configure BCM to send alerts when a GPU's temperature exceeds a critical threshold. Where would you configure this alerting policy within BCM?
- A. In the 'bcm_config.yaml' file.
- B. Using the 'nvidia-smi' command-line tool to set temperature thresholds and trigger alerts.
- C. By creating a custom Prometheus rule and integrating it with BCM.
- D. Through the BCM web interface, in the 'Alerting Policies' section.
- E. Within the DCGM configuration files on the GPU nodes.
Answer: D
Explanation:
BCM provides a dedicated 'Alerting Policies' section in its web interface where you can define rules and thresholds for various metrics, including GPU temperature. You can configure the specific threshold, the alert severity, and the notification channels (e.g., email, Slack). Other options are either not directly supported or are more complex and less integrated.
NEW QUESTION # 46
An AI model deployed through Fleet Command exhibits a vulnerability. You must urgently patch all edge devices with the updated model.
What is the fastest and safest way to accomplish this, minimizing disruption to ongoing operations?
- A. Employ a staged rollout strategy within Fleet Command, gradually updating subsets of devices while monitoring for any issues before proceeding to the entire fleet.
- B. Use Fleet Command to orchestrate an over-the-air (OTA) update of the model to all devices simultaneously, potentially causing temporary service interruption.
- C. Inform users to manually download and install the patch to all edge devices.
- D. Individually SSH into each device and manually replace the model files.
- E. Immediately shut down all edge devices to prevent further exploitation and then update the model offline.
Answer: A
Explanation:
A staged rollout provides the best balance between speed and safety. It allows for early detection of potential issues during the update process, minimizing the risk of widespread disruption. Manual intervention (A) is too slow. A simultaneous update (B) could cause a large outage if problems arise. Shutting down all devices (D) is overly disruptive. User manual install (E) is not reliable or centrally controlled.
NEW QUESTION # 47
You're using BCM to manage a cluster and need to upgrade the Kubernetes version. What considerations are critical to ensure a smooth upgrade process?
- A. Ensure all worker nodes have sufficient resources (CPU, memory) for the new Kubernetes version.
- B. Update the NVIDIA drivers and container runtime on all nodes after the Kubernetes upgrade.
- C. Back up the etcd database before starting the upgrade.
- D. Test the upgrade in a staging environment before applying it to the production cluster.
- E. Drain nodes before upgrading them to minimize application downtime.
Answer: A,C,D,E
Explanation:
Backing up etcd is crucial for rollback. Resource sufficiency prevents upgrade failures. Testing in staging identifies potential issues. Draining minimizes downtime. NVIDIA drivers should ideally be checked for compatibility and potentially updated before the Kubernetes upgrade.
NEW QUESTION # 48
When using GPUDirect RDMA for inter-GPU communication, what component MUST be supported by the network interface card (NIC) to ensure optimal performance?
- A. Remote Direct Memory Access (RDMA)
- B. TCP Offload Engine (TOE)
- C. Jumbo Frames
- D. Ethernet Flow Control
- E. Quality of Service (QOS)
Answer: A
Explanation:
GPUDirect RDMA requires RDMA support on the NIC. RDMA enables direct memory access between GPUs without CPU intervention, significantly reducing latency and improving bandwidth. While other features like TOE, QOS, flow control, and Jumbo Frames can contribute to overall network performance, they are not fundamental requirements for GPUDirect RDMA to function.
NEW QUESTION # 49
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