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NVIDIA NCP-AII Exam Syllabus Topics:

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

NVIDIA AI Infrastructure Sample Questions (Q48-Q53):

NEW QUESTION # 48
During multi-node HPL burn-in, GPUs show uneven utilization. Which configuration ensures balanced workload distribution?

Answer: C

Explanation:
Uneven GPU utilization in a multi-node cluster is a classic symptom ofPCIe/NUMA imbalance. In a DGX H100, each set of GPUs is physically closer to a specific CPU socket and a specific set of high-speed NICs. If the workload (HPL) is launched without strict affinity, an MPI rank running on a CPU core attached to Socket
0 might attempt to control a GPU attached to Socket 1. This forces data to cross the inter-processor links (UPI
/QPI), which have significantly higher latency and lower bandwidth than a direct PCIe path. This
"bottlenecking" causes some GPUs to wait for data longer than others, leading to the uneven utilization observed. The verified solution is to use an orchestration script or flags that enforceAffinity. By setting --gpu- affinity and --cpu-affinity, the administrator ensures that each GPU is managed by a CPU core on its local NUMA node. This alignment minimizes latency and ensures that every GPU in the cluster receives data at the same rate, resulting in the flat, high-utilization profile required for a successful HPL burn-in record.


NEW QUESTION # 49
Consider the following 'ibroute' command used on an InfiniBand host: 'ibroute add dest Oxla dev ib0'. What is the MOST likely purpose of this command?

Answer: E

Explanation:
The 'ibroute add dest Ox1a dev ibC command creates a static route for traffic destined for the InfiniBand LID (Local Identifier) Ox1a, using the InfiniBand interface named 'ib0'. InfiniBand routing is primarily based on LIDS, not IP addresses directly (though IP over 1B is possible). The 'dest' parameter specifies the destination LID.


NEW QUESTION # 50
A critical AI model training job consistently fails on a specific GPU server in your cluster after running for approximately 24 hours.
Monitoring data shows a sudden drop in GPU power consumption followed by a system reboot. All other GPUs on the server appear normal. The server has redundant PSUs. What is the MOST likely cause?

Answer: A

Explanation:
Thermal runaway (B) is the most probable cause. The 24-hour delay suggests a gradual heat buildup. A failing TIM would cause the GPU to overheat until it triggers a thermal shutdown, resulting in the power drop and reboot. While a PSU issue (C) is possible, redundant PSUs should prevent a complete failure unless one PSU is completely dead and the second PSU is overloaded by the entire load for a short period. The other options are less likely to cause this specific failure pattern.


NEW QUESTION # 51
Which of the following are key benefits of using NVIDIA Spectrum-X switches in an A1 infrastructure compared to traditional Ethernet switches? (Select THREE)

Answer: C,D,E

Explanation:
Spectrum-X switches are designed for high-performance computing and A1 workloads. They support RoCE and InfiniBand for low- latency communication, offer advanced telemetry for network optimization, and include hardware-based acceleration for collective communication operations, improving the efficiency of distributed A1 training. While Spectrum-X supports IPv6, this is also a common feature in modern Ethernet switches. Spectrum-X switches typically have a higher cost per port compared to basic Ethernet switches due to their advanced features and performance.


NEW QUESTION # 52
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?

Answer: B

Explanation:
While object storage is excellent for massive scale and metadata, NVIDIA AI infrastructure best practices for training workloads-especially video analysis-heavily prioritizeParallel File Systems (PFS). Modern AI frameworks (PyTorch, TensorFlow) and NVIDIA's own SDKs (like DeepStream or NeMo) are optimized to read from POSIX-compliant file systems. For video content analysis, the training process involves "sharding" large video files and performing random-access reads across a massive dataset. A high-performance file system (such as Lustre, Weka, or IBM Storage Scale) provides the high throughput and low-latency metadata operations required to keep 8 or more H100 GPUs per node saturated with data. File storage allows for the hierarchical organization that data scientists use to manage datasets (e.g., /datasets/train/videos/) and supports GPUDirect Storage (GDS), which allows the GPU to pull data directly from the storage fabric into GPU memory, bypassing the CPU to maximize ingestion throughput.


NEW QUESTION # 53
......

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