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| Section | Weight | Objectives |
|---|
| Validation, Troubleshooting and Optimization | 20% | - Troubleshooting and maintenance
- 1. Performance optimization and best practices
- 2. Hardware and software fault isolation
- Cluster validation and benchmarking
- 1. HPL, NCCL and performance testing
- 2. Health checks and error detection
|
| Networking and Storage Configuration | 20% | - NVIDIA networking solutions
- 1. InfiniBand and Ethernet fabric setup
- 2. BlueField DPU configuration
- Storage integration
- 1. Storage performance for AI workloads
- 2. Parallel file systems and object storage
|
| GPU Resource Management | 15% | - GPU scheduling and optimization
- 1. Workload placement and sharing
- 2. NVLink and fabric management
- Multi-Instance GPU (MIG) configuration
- 1. Isolation and performance tuning
- 2. Partitioning and resource allocation
|
| System and Server Bring-up | 20% | - Firmware and system configuration
- 1. OS installation and base configuration
- 2. BMC, BIOS, TPM and firmware updates
- Hardware installation and validation
- 1. Power, cooling and physical connectivity verification
- 2. Server, GPU, network and storage components setup
|
| Software Stack Deployment | 25% | - NVIDIA software components
- 1. Base Command Manager and cluster management tools
- 2. GPU drivers, container toolkit and runtime
- Orchestration and workload management
- 1. NGC catalog and software deployment
- 2. Slurm, Kubernetes and container orchestration
|
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NCP-AII Exam Details & NCP-AII Exam Passing Score
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NVIDIA AI Infrastructure Sample Questions (Q24-Q29):
NEW QUESTION # 24
Which of the following statements are correct regarding the use of NVIDIA GPUs with Docker containers?
- A. The NVIDIA Container Toolkit allows you to run GPU-accelerated applications in Docker containers without modifying the container image.
- B. Using environment variables like 'CUDA VISIBLE DEVICES' within the container can influence which GPUs are accessible to the application.
- C. CUDA libraries are required inside the container if your application uses CODA.
- D. You must install NVIDIA drivers inside the Docker container to enable GPU support.
- E. The 'nvidia-smr command can only be run on the host machine, not inside a Docker container.
Answer: A,B,C
Explanation:
The NVIDIA Container Toolkit allows GPU-accelerated apps to run in Docker without altering the image. The host's drivers are leveraged. CUDA libraries are necessary inside the container if your app uses CUDA. is used to control GPU visibility within the container. Drivers are not needed inside the container because they're managed by the host (making B incorrect), and 'nvidia-smi' can be run inside containers if the NVIDIA Container Toolkit is properly set up (making C incorrect).
NEW QUESTION # 25
An infrastructure engineer in an AI factory has successfully replaced a power supply unit on an NVIDIA DGX H100. After installation, both the IN and OUT LEDs on the new power supply illuminate solid green.
Which NVSM CLI command should the engineer use to quickly verify the overall system status and ensure it is operating as expected?
- A. nvsm show powermode
- B. nvsm show power
- C. nvsm show alerts
- D. nvsm show health
Answer: D
Explanation:
The NVIDIA System Management (NVSM) tool is the definitive CLI utility for monitoring the health of DGX platforms. While replacing a PSU (Power Supply Unit) is a common maintenance task, verifying that the new component is correctly integrated into the system's health model is mandatory. While nvsm show power would provide specific data regarding wattage and voltage for the PSU, the most comprehensive way to ensure the replacement hasn't caused secondary issues or that the system hasn't remained in a "Degraded" state is to run nvsm show health. This command performs a global check across all subsystems: GPUs, NVLink switches, storage, fans, and power. If the PSU replacement was successful and the system is back to full redundancy, nvsm show health will return a "Healthy" status. In an AI factory setting, where DGX H100 nodes pull significant power, ensuring that all 6 PSUs (in an N+N or N+1 configuration) are not only physically green but logically acknowledged by the Baseboard Management Controller (BMC) is critical for preventing unexpected shutdowns during high-load training iterations.
NEW QUESTION # 26
One of the nodes in a cluster is not running as fast as the others and the system administrator needs to check the status of the GPUs on that system. What command should be used?
- A. iblinkinfo
- B. lspci | grep NVIDIA
- C. nvidia-gpu-status
- D. nvidia-smi
Answer: D
Explanation:
nvidia-smi is the standard NVIDIA command-line utility for checking GPU status, including utilization, temperature, power draw, memory usage, driver state, and running processes. It is the appropriate first command to investigate why a GPU node is performing slower than others.
NEW QUESTION # 27
After deploying BlueField OS, you notice that the network interfaces are not automatically configured with IP addresses. Which of the following actions would be the MOST appropriate first step to troubleshoot this issue?
- A. Check the DHCP client configuration to ensure it is enabled and properly configured to request IP addresses. Examine the logs for any errors.
- B. Restart the networking service using 'systemctl restart networking'.
- C. Re-flash the Bluefield OS image
- D. Reinstall the Mellanox OFED drivers. A corrupted driver installation could cause network configuration issues.
- E. Manually assign static IP addresses to the interfaces using the 'ifconfig' command.
Answer: A
Explanation:
In most modern systems, network interfaces are automatically configured using DHCP. Therefore, the first step is to check if the DHCP client is enabled and configured correctly. If DHCP fails, then other troubleshooting steps, such as static IP assignment or driver reinstallation, can be considered.
NEW QUESTION # 28
You are designing an Ai server infrastructure using NVIDIA HGX AIOO modules. The server's power supply units (PSUs) are configured in a redundant (N+1) setup. The individual PSUs are rated for 3000W each, and the server contains three PSUs. If the expected peak power consumption of the HGX A100 modules and other components is 5500W, what is the safety margin (in Watts) in the power budget?
- A. 500W
- B. 0W
- C. 5000W
- D. 3500W
- E. 9000W
Answer: A
Explanation:
With N+1 redundancy and three 3000W PSUs, the total available power is 2 3000W = 6000W (N+1 means the system can tolerate one PSU failure). The safety margin is 6000W - 5500W = 500W.
NEW QUESTION # 29
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