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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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 4	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 5	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.

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NVIDIA AI Infrastructure Sample Questions (Q82-Q87):

NEW QUESTION # 82
A system administrator needs to change the RAID level of DGX station and use the script included in the DGX software with no data on the array. What are the options the administrator can choose?

A. RAID 0 and RAID 5
B. RAID 1 and RAID 5
C. RAID 10 and RAID 5
D. RAID 0 and RAID 1

Answer: A

Explanation:
The DGX Station RAID configuration script supports configuring the data array as RAID 0 or RAID 5. RAID 0 provides maximum performance and capacity, while RAID 5 provides fault tolerance with usable capacity tradeoff.

NEW QUESTION # 83
A media company is developing an AI platform for video content analysis that requires storing and processing large volumes of unstructured video data. The platform must support high throughput for data ingestion and provide efficient access for real-time analytics. Given these requirements, which storage strategy should the company implement?

A. Object storage for scalability and metadata management
B. Block storage for low latency and high performance
C. File storage for hierarchical organization and easy navigation
D. Tape storage for its cost-effectiveness and archival capabilities

Answer: A

Explanation:
Object storage is best suited for large volumes of unstructured video data because it scales efficiently, supports high-throughput ingestion, and uses rich metadata to organize and retrieve content for analytics workflows. This makes it a strong fit for AI-based video analysis and real- time data access patterns.

NEW QUESTION # 84
You're deploying a new cluster with multiple NVIDIAAIOO GPUs per node. You want to ensure optimal inter-GPU communication performance using NVLink. Which of the following configurations are critical for achieving maximum NVLink bandwidth?

A. The NVIDIA driver must be configured to enable NVLink; it is disabled by default.
B. GPUs should be physically installed in slots that maximize direct NVLink connections based on the server's architecture.
C. The motherboard must support PCle Gen5 to maximize NVLink bandwidth.
D. All GPUs within a node must be the same model and have identical firmware versions.
E. The server must use a specific CPU model to leverage NVLink capabilities.

Answer: B,D

Explanation:
For optimal NVLink performance, several conditions must be met. GPUs of the same model and firmware ensure compatibility and prevent performance bottlenecks. Physical placement is critical; GPUs must be installed in slots that maximize direct NVLink connections, as defined by the server's architecture and documentation. While PCle Gen5 is beneficial for overall system performance, it does not directly impact NVLink bandwidth. NVLink is typically enabled by default. Some CPU models may be preferable, but it's the motherboard's NVLink topology that is more important.

NEW QUESTION # 85
An A1 inferencing server, using NVIDIA Triton Inference Server, experiences intermittent crashes under peak load. The logs reveal CUDA out-of-memory errors (00M) despite sufficient system RAM. You suspect a GPU memory leak within one of the models. Which strategy BEST addresses this issue?

A. Reduce the batch size and concurrency of the offending model in the Triton configuration.
B. Disable other models running on the same GPU to free up memory.
C. Implement CUDA memory pooling within the Triton Inference Server configuration to reuse memory allocations efficiently.
D. Increase the system RAM to accommodate the growing memory footprint.
E. Upgrade the GPUs to models with larger memory capacity.

Answer: A,C

Explanation:
Options B and C directly address the 00M issue. CUDA memory pooling enables efficient reuse of GPU memory, minimizing allocations and deallocations. Reducing batch size and concurrency decreases the memory footprint of the model, alleviating the pressure on GPU memory. While upgrading GPUs (D) is a solution, it is more costly than optimizing the current configuration. Increasing system RAM (A) does not solve GPU memory issues. Disabling other models (E) reduces load but doesn't address the core problem of the memory leak in the first place.

NEW QUESTION # 86
When installing multiple NVIDIA GPUs, which of the following factors are MOST important to consider regarding PCIe slot configuration?
(Choose two)

A. Ensure all GPUs are installed in slots of the same color.
B. Install the GPUs in the lowest numbered slots first.
C. Ensure all GPUs have the same PCIe generation (e.g. Gen4).
D. Ensure each GPU is installed in a slot with sufficient PCIe lanes (e.g., x16).
E. Ensure the PCIe slots are directly connected to the CPU for optimal bandwidth.

Answer: D,E

Explanation:
The number of PCIe lanes directly impacts bandwidth. Direct CPU connection minimizes latency. Slot color and numbering are usually irrelevant. Same PCIe Gen isn't critical as long as minimum requirements are met.

NEW QUESTION # 87
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