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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Monitoring and Management	15-20%	- Resource utilization - NVIDIA management tools - Troubleshooting basics - Performance monitoring
Topic 2: NVIDIA AI Infrastructure Components	25-30%	- NVIDIA AI Enterprise software - Storage solutions for AI workloads - NVIDIA networking solutions (Mellanox) - NVIDIA DGX systems
Topic 3: Security and Best Practices	10-15%	- Operational best practices - Security fundamentals - Compliance considerations
Topic 4: Deployment and Configuration	25-30%	- Software deployment - Cluster configuration - Network configuration - System installation and setup
Topic 5: AI Infrastructure Fundamentals	15-20%	- GPU architecture basics - Data center infrastructure requirements - NVIDIA software stack overview - AI and Deep Learning concepts

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NVIDIA AI Infrastructure Sample Questions (Q34-Q39):

NEW QUESTION # 34
A System Administrator needs to change the scheduling behavior of a single GPU to use a fixed share scheduler. What command achieves this?

A. esxcli -i 0 -mig 18
B. esxcli system module parameters set -m nvidia -p
C. nvidia-smi -i 0 -mig 1
D. mixconfig -d /dev/mst/mt4123_pciconf0 set LINK_TYPE_P1=2

Answer: B

Explanation:
Fixed-share vGPU scheduling is configured through the NVIDIA driver module parameters on ESXi using esxcli system module parameters set -m nvidia -p .... The parameter must specify the fixed-share scheduler value, typically using NVreg_RegistryDwords with the appropriate RmPVMRL setting.

NEW QUESTION # 35
A data scientist needs to run a Jupyter notebook on specific GPU cards. What command should be used?

A. docker run --dpu "1,2" -p 8888:8888 jupyter/scipy-notebook:2023-09-25
B. docker run --runtime=nvidia -dpu "1,2" -p 8888:8888 jupyter/scipy-notebook:2023-09-25
C. docker run --gpus I"device=UUID-ABCDEF,1" -p 8888:8888 jupyter/scipy-notebook:2023-09-25
D. docker run --gpus all --limit-gpu ""device=UUID-ABCDEF" -p 8888:8888 jupyter/scipy- notebook:2023-09-25

Answer: C

Explanation:
Docker uses the --gpus flag with a device= selector to expose only specific GPUs to a container.
The selector can target GPUs by UUID and index, allowing the Jupyter notebook container to run only on the specified GPU cards while publishing port 8888 for notebook access.

NEW QUESTION # 36
You're optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You've deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.
What are potential bottlenecks and optimization strategies? (Select all that apply)

A. Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.
B. The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
C. Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.
D. The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).
E. Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.

Answer: B,C,D,E

Explanation:
Multiple factors can contribute to low throughput in inference serving. Model loading overhead is significant, and dynamic batching is crucial to maximize throughput. Insufficient CPU cores and memory constraints on the GPU also limit performance. Model precision reduction helps reduce memory footprint and increase throughput. While PCle bandwidth is a factor, it is often not the primary bottleneck in inference serving.

NEW QUESTION # 37
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 alerts
B. nvsm show powermode
C. nvsm show health
D. nvsm show power

Answer: C

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 # 38
For a 48-hour NCCL burn-in test, which parameters ensure sustained fabric stress while detecting silent data corruption?

A. all_reduce_perf -b 8G -e 32G -z 1 -G 1000
B. reduce_scatter_perf -f 2 -g 8
C. broadcast_perf -b 4G -e 16G -w 100
D. all_reduce_perf -b 8G -e 32G -c 1000 -z 1 -G 1000

Answer: D

Explanation:
all_reduce_perf with large message sizes sustains heavy fabric stress, while the validation/checking option helps detect silent data corruption during the burn-in. The long iteration count and GPU Direct usage keep the NCCL workload active long enough to expose intermittent fabric or data-integrity issues.

NEW QUESTION # 39
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