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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 2	Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm Enroot Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.
Topic 3	System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC OOB TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Topic 4	Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD Intel servers and storage.
Topic 5	Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.

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NVIDIA AI Infrastructure Sample Questions (Q98-Q103):

NEW QUESTION # 98
The system administrator plans to use Multi-instance GPU profiles. What command should be used to verify that the GPU has this mode enabled?

A. nvidia-mode
B. nvidia-smi
C. nvidia-mig
D. nvidia-enable

Answer: B

Explanation:
nvidia-smi is the standard NVIDIA management command used to verify whether MIG mode is enabled on a supported GPU. It displays the GPU's MIG status and can also show configured MIG instances and profiles.

NEW QUESTION # 99
An administrator needs to perform a comprehensive pre-production stress test on a DGX H100 system. Which command validates GPU, CPU, memory, and storage components while following NVIDIA's recommended procedure?

A. nvidia-smi -q | grep " GPU Stress Test "
B. stress --cpu $(nproc) --io $(nproc) --timeout 600
C. sudo nvsm stress-test --force
D. ./gpu_burn 60

Answer: C

Explanation:
The correct command is sudo nvsm stress-test --force. NVIDIA recommends using NVSM for the DGX pre- flight stress test before putting a DGX H100 system into production or after servicing. The documented NVSM stress test can run checks across supported components, including GPUs, CPU, memory, and storage, and the recommended command for all supported components is sudo nvsm stress-test --force. nvidia-smi -q provides detailed GPU information, but it does not execute a full platform stress test. The Linux stress command can load CPU and I/O subsystems, but it is generic and does not validate the DGX platform using NVIDIA's health model. gpu_burn may stress GPUs, but it does not cover CPU, system memory, storage, and DGX-specific platform checks in the recommended way. During server bring-up, NVSM is preferred because it understands DGX hardware components and can identify platform health issues before the node is released to production workloads.

NEW QUESTION # 100
You are deploying a new A1 inference service using Triton Inference Server on a multi-GPU system. After deploying the models, you observe that only one GPU is being utilized, even though the models are configured to use multiple GPUs. What could be the possible causes for this?

A. Insufficient CPU cores are available for the Triton Inference Server, limiting its ability to spawn multiple inference processes.
B. The Triton Inference Server is not configured to enable CUDA Multi-Process Service (MPS).
C. The model configuration file does not specify the 'instance_group' parameter correctly to utilize multiple GPUs.
D. The GPUs are not of the same type and Triton cannot properly schedule across them.
E. The models are not optimized for multi-GPU inference, resulting in a single GPU bottleneck.

Answer: B,C

Explanation:
The 'instance_group' parameter in the model configuration dictates how Triton distributes the model across GPUs. Without proper configuration, it may default to a single GPIJ. CUDA MPS allows multiple CUDA applications (in this case, Triton inference processes) to share a single GPU, improving utilization. Insufficient CPU cores or non-optimized models could limit performance, but wouldn't necessarily restrict usage to a single GPIJ. While dissimilar GPIJs can affect performance, Triton will attempt to schedule across them if configured correctly.

NEW QUESTION # 101
You are tasked with optimizing storage performance for a deep learning training job on an NVIDIA DGX server. The training data consists of millions of small image files. Which of the following storage optimization techniques would be MOST effective in reducing I/O bottlenecks?

A. Implementing a tiered storage system with NVMe drives for frequently accessed data and HDDs for less frequently accessed data.
B. Enabling data compression on the storage volume.
C. Implementing RAID 0 across all storage devices.
D. Using a distributed file system with data striping across multiple storage nodes.
E. Increasing the block size of the file system to the maximum supported value.

Answer: D

Explanation:
A distributed file system with data striping (option B) is the most effective because it parallelizes I/O operations across multiple storage nodes, reducing the load on any single storage device and improving overall throughput for many small files. RAID 0 (A) improves read/write speeds but offers no redundancy. Compression (C) can reduce storage space but adds overhead. Increasing block size (D) is beneficial for large files, but not necessarily for numerous small files. Tiered storage (E) can help, but distributing the file system is the priority for numerous small files.

NEW QUESTION # 102
After successfully installing the NVIDIA Container Toolkit and configuring the Docker runtime, you attempt to run a container that requires GPU access. However, the container fails to start with an error indicating that no GPUs are detected. You've verified that 'nvidia-smi' works on the host. Which of the following could be potential causes for this issue? (Select all that apply)

A. The Docker daemon was not restarted after configuring the NVIDIA runtime.
B. The '-gpus all' flag was not included when running the 'docker run' command.
C. The NVIDIA Container Toolkit package is corrupted and needs to be reinstalled.
D. The container image itself is missing the necessary CUDA libraries.
E. The NVIDIA drivers are not compatible with the kernel version running on the host.

Answer: A,B,E

Explanation:
Several factors can cause this issue. A driver-kernel incompatibility (A) prevents the NVIDIA drivers from properly communicating with the hardware. Forgetting to restart the Docker daemon (B) means the configuration changes applied by 'nvidia-ctk' are not active. The '-gpus all' (or equivalent) flag (C) is mandatory to explicitly request GPU resources for the container. Corrupted toolkit (D) would likely present installation failures earlier. Missing CUDA libraries (E) would likely lead to runtime errors within the container, not a failure to detect the GPUs in the first place.

NEW QUESTION # 103
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