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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional: AI Operations Exam |
| Exam Number: | NCP-AIO |
| Certificate Validity Period: | 2 years |
| Passing Score: | Pass/Fail (not officially disclosed) |
| Exam Duration: | 120 minutes |
| Related Certifications: | NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional: AI Infrastructure (NCP-AII) NVIDIA-Certified Professional: AI Networking (NCP-AIN) |
| Real Exam Qty: | 30–75 |
| Exam Format: | Scenario-based, Hands-on lab exercises, Multiple choice |
| Exam Price: | $500 USD |
| Available Languages: | English |
| Recommended Training: | NVIDIA AI Operations Training |
| Exam Registration: | Certiverse Exam Platform NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCP-AIO Sample Questions |
| Exam Way: | Online remote proctored exam |
| Pre Condition: | Recommended: 2–3 years of experience managing AI infrastructure, GPU systems, or data center operations; familiarity with Kubernetes, containers, and NVIDIA software stack |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/ai-operations-professional/ |
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NEW QUESTION # 27
You're setting up a Kubernetes cluster on NVIDIA DGX servers using Bare Metal Container (BCM). During the pre-flight checks, the 'kubelet' fails to start on one of the worker nodes. The logs indicate a problem with device plugin registration. Which of the following is the MOST likely cause and the best initial troubleshooting step?
Answer: C
Explanation:
The NVIDIA Container Toolkit is essential for exposing GPU devices to containers within Kubernetes. A missing or misconfigured toolkit is the most common reason for device plugin registration failures. Checking its installation and configuration is the crucial first step. Incorrect driver version (A) could be an issue but less likely. Firewall (B) and SELinux (C) are also possibilities, but Toolkit (D) is most direct. CPU resources (E) are unlikely to cause device registration issues.
NEW QUESTION # 28
A Fleet Command system administrator wants to create an organization user that will have the following rights:
For locations - read only
For Applications - read/write/admin
For Deployments - read/write/admin
For Dashboards - read only
What role should the system administrator assign to this user?
Answer: D
Explanation:
The Fleet Command Operator role is designed to provide users with read-only access to locations and dashboards while granting full read/write/admin rights for applications and deployments. This matches the described access requirements where the user can manage applications and deployments but only view locations and dashboards without modification rights.
Other roles like Fleet Command Admin have broader permissions, Supporter has more limited access, and Viewer is primarily read-only for all resources.
NEW QUESTION # 29
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?
Answer: D
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 # 30
A system administrator wants to run these two commands in Base Command Manager.
main showprofile device status apc01
What command should the system administrator use from the management node system shell?
Answer: C
Explanation:
The Base Command Manager command shell (cmsh) accepts the -c flag to execute multiple commands sequentially. Using cmsh -c "main showprofile; device status apc01" runs the main showprofile followed by device status apc01 commands in one invocation, allowing scripted or batch execution from the management node shell.
NEW QUESTION # 31
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?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIAFleet Commandis a cloud-native software platform designed to deploy, manage, and orchestrate AI applications at the edge. When managing multiple AI applications on the same GPU,Multi-Instance GPU (MIG) supportis critical. MIG allows a single GPU to be partitioned into multiple independent instances, each with dedicated resources (compute, memory, bandwidth), enabling workload isolation and preventing interference between applications.
* Remote Consoleallows remote access for management but does not provide GPU resource isolation.
* Secure NFS supportis for secure network file system sharing, unrelated to GPU resource partitioning.
* Over-the-air updatesare for updating software remotely, not for GPU resource management.
Therefore, to ensure application isolation on the same GPU in Fleet Command environments, enablingMIG support (option C)is the recommended and standard practice.
This capability is emphasized in NVIDIA's AI Operations and Fleet Command documentation for managing edge AI deployments efficiently and securely.
NEW QUESTION # 32
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