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NVIDIA NCP-AIO Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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 3

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 4

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.

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NVIDIA AI Operations Sample Questions (Q17-Q22):

NEW QUESTION # 17
The 'nvsm' service is consistently crashing on one of your nodes. Analyzing the core dump reveals a segmentation fault related to memory access within the NVSwitch driver. What is the MOST appropriate course of action?

A. Increase the swap space on the node.
B. Report the issue to NVIDIA support with the core dump and relevant system information.
C. Recompile the NVSwitch driver with debugging symbols.
D. Try different versions of CUDA.
E. Disable the NVSwitch on the affected node.

Answer: B

Explanation:
Segmentation faults related to driver code usually indicate a bug within the driver itself. Reporting the issue with a core dump allows NVIDIA engineers to investigate and provide a fix. Trying to debug the driver yourself (recompiling) or disabling the NVSwitch are less effective solutions for this type of problem. Different versions of CUDA also can cause problems, but first report with the core dump.

NEW QUESTION # 18
You are deploying a VMI container on a cloud platform that supports both NVIDIA vGPU and passthrough GPU access. Your workload requires maximum GPU performance and is not shared with other users. Which GPU access method is generally recommended for this scenario?

A. It doesn't matter; both vGPU and Passthrough offer the same performance.
B. GPU Passthrough
C. Shared GPU using CUDA MPS (Multi-Process Service)
D. Software Emulation of the GPU
E. NVIDIA vGPU

Answer: B

Explanation:
GPU passthrough provides direct access to the physical GPU, resulting in the highest possible performance and minimal overhead. vGPU introduces a virtualization layer, potentially reducing performance slightly. CUDA MPS allows for shared GPU access, which is not required in this scenario.

NEW QUESTION # 19
A user reports that their Docker container, which utilizes a specific GPU, is consistently slower than expected when performing inference. You need to diagnose whether the GPU is being utilized effectively. Which of the following approaches are MOST effective?

A. Use 'docker stats' to monitor the container's CPU and memory usage. High CPU usage might indicate a CPU bottleneck.
B. Run 'nvidia-smi on the host to see the CUDA version and driver details to check compatibility issues.
C. Use 'nvidia-smi' inside the container to monitor GPU utilization, memory usage, and temperature during inference.
D. Monitor network I/O using tools like Siftop' or 'tcpdump' to check for network-related bottlenecks.
E. Profile the application code using profiling tools like 'nvprof or 'NVIDIA Nsight Systems to identify performance bottlenecks on the GPU.

Answer: A,C,E

Explanation:
'nvidia-smi' within the container directly reveals GPU utilization. 'docker state helps identify general resource constraints (like CPU bottlenecks). Profiling tools (C) provide detailed insights into GPU code performance. Checking CUDA version is good for debugging, however, its effect is not direct to the speed of the application.

NEW QUESTION # 20
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 # 21
You are managing a cluster with multiple NVIDIA GPUs. A user reports that their deep learning training job is running slower than expected. Which of the following system management tools would provide the MOST direct insight into potential GPU bottlenecks?

A. 'free -m' command in Linux
B. nvidia-smi' (NVIDIA System Management Interface)
C. 'top' command in Linux
D. aux' command in Linux
E. 'df -m command in Linux

Answer: B

Explanation:
'nvidia-smi' is specifically designed to monitor NVIDIA GPUs, providing information on utilization, memory usage, temperature, and power consumption, allowing for quick identification of GPU-related bottlenecks. The other tools provide system-level information but not GPU- specific details.

NEW QUESTION # 22
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