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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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.

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NVIDIA AI Infrastructure Sample Questions (Q49-Q54):

NEW QUESTION # 49
After updating BlueField-3 DPU BMC firmware via Redfish, the engineer observes "TaskState:
Running" but no progress after 15 minutes. How should they track the update's completion status?

Answer: A

Explanation:
Redfish firmware updates create an asynchronous task that must be monitored through the task service using the returned Task ID. Querying that Task ID on the DPU BMC shows whether the installation is still running, completed successfully, or failed, without interrupting the update process.


NEW QUESTION # 50
You're optimizing an Intel Xeon server with 4 NVIDIAAIOO GPUs for a computer vision application that uses CODA. You notice that the GPU utilization is fluctuating significantly, and performance is inconsistent. Using 'nvprof, you identify that there are frequent stalls in the CUDA kernels due to thread divergence. What are possible causes and solutions?

Answer: A,B

Explanation:
Thread divergence occurs when threads within the same warp take different execution paths due to conditional branches, leading to serialization. Re-writing code to minimize branching is critical. Memory alignment ensures that threads access memory efficiently and doesn't cause thread divergence stalls, which is often misaligned. Thermal throttling is a possible cause of fluctuating utilization but doesn't directly explain the stalls identified in the profiler. Compiler flags and driver version issues can cause performance problems but are less likely to cause frequent thread divergence stalls.


NEW QUESTION # 51
On a DCX, a system administrator needs to monitor PSU, CPU Utilization, GPU Utilization, RAID, and Memory Utilization. What single NVIDIA tool should be used?

Answer: D

Explanation:
NVSM is the NVIDIA System Management tool for DGX systems and provides system-level monitoring across components such as power supplies, CPU utilization, GPU utilization, RAID/storage status, and memory utilization from a single management interface.


NEW QUESTION # 52
You've flashed the BlueField OS to your SmartNlC, but you need to customize the kernel command line arguments (bootargs) to enable a specific feature. Where is the MOST appropriate place to modify these arguments for persistent changes that survive reboots?

Answer: A

Explanation:
The bootloader configuration file (extlinux.conf, grub.cfg, uEnv.txt depending on the system) is where boot arguments are persistently stored. Modifying the kernel image directly is highly discouraged and risky. 'letc/default/grub' is a common location on standard Linux systems, but not necessarily on the BlueField OS's boot environment. '/proc/cmdline' shows the currently used arguments, but modifying it doesn't persist changes across reboots. bfboot will only change the image during that flash, changes at the bootloader level persist after subsequent flashes.


NEW QUESTION # 53
You have a server with 8 NVIDIA A100 GPUs. You want to configure each GPU to be used by a different user, ensuring resource isolation and preventing one user's workload from monopolizing the entire GPU. Which NVIDIA technology is most suitable for this scenario?

Answer: C

Explanation:
NVIDIA MIG (Multi-lnstance GPU) is designed specifically for this scenario. It allows partitioning a single physical GPU into multiple isolated GPU instances, each with its own dedicated memory, compute, and isolation. CUDA MPS allows multiple CUDA applications to share a single GPU but does not provide the same level of resource isolation as MIG. vGPU is primarily for virtualized environments. SLI and NVLink are for GPU interconnection, not resource isolation.


NEW QUESTION # 54
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