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Vendor: | NVIDIA |
---|---|
Exam Code: | NCP-AIO |
Exam Name: | AI Operations |
Exam Questions: | 66 |
Last Updated: | October 7, 2025 |
Related Certifications: | NVIDIA-Certified Professional |
Exam Tags: | Professional NVIDIA System Administrators and AI Infrastructure Engineers |
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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?
Comprehensive and Detailed Explanation From Exact Extract:
In an air-gapped environment where the DPU has no internet connectivity, direct pulling of container images from NVIDIA's NGC catalog is not possible. The recommended approach is to manually download the required container image and YAML deployment files from a connected system, then transfer these files to the DPU. Deployment is then performed using Kubernetes with a standalone Kubelet on the DPU, which can deploy the preloaded container image offline. This ensures the deployment proceeds successfully without internet access.
You are setting up a Kubernetes cluster on NVIDIA DGX systems using BCM, and you need to initialize the control-plane nodes.
What is the most important step to take before initializing these nodes?
Comprehensive and Detailed Explanation From Exact Extract:
Disabling swap on all control-plane nodes is a critical prerequisite before initializing Kubernetes control-plane nodes. Kubernetes requires swap to be disabled to maintain performance and stability. Failure to disable swap can cause kubeadm initialization to fail or lead to unpredictable cluster behavior.
A system administrator needs to collect the information below:
GPU behavior monitoring
GPU configuration management
GPU policy oversight
GPU health and diagnostics
GPU accounting and process statistics
NVSwitch configuration and monitoring
What single tool should be used?
Comprehensive and Detailed Explanation From Exact Extract:
The NVIDIA Data Center GPU Manager (DCGM) is the comprehensive management tool that provides all the requested functionalities: monitoring GPU behavior, managing configurations, enforcing policies, health diagnostics, process accounting, and NVSwitch monitoring. DCGM is designed for large-scale GPU management in data centers and AI clusters, providing detailed telemetry and control over NVIDIA GPUs and NVSwitches.
nvidia-smi provides GPU monitoring but lacks full policy and NVSwitch management.
CUDA Toolkit is for GPU programming and development.
Nsight Systems is focused on performance profiling and debugging.
Therefore, DCGM is the single tool that meets all the listed requirements.
What two (2) platforms should be used with Fabric Manager? (Choose two.)
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.
A system administrator needs to optimize the delivery of their AI applications to the edge.
What NVIDIA platform should be used?
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA Fleet Command is the platform designed specifically to optimize and manage the deployment and delivery of AI applications at the edge. It enables secure and scalable orchestration of AI workloads across distributed edge devices, providing lifecycle management, remote monitoring, and updates. Fleet Command facilitates running AI applications closer to where data is generated (edge), improving latency and operational efficiency.
Base Command Platform and Base Command Manager primarily target data center and AI cluster management for configuration, monitoring, and troubleshooting.
NetQ is focused on network telemetry and network state monitoring rather than application delivery.
Therefore, for AI application delivery and optimization at the edge, Fleet Command is the recommended NVIDIA platform.
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