Quiz 2026 NVIDIA NCP-AII–High Hit-Rate Free Sample Questions

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NVIDIA NCP-AII Exam Syllabus Topics:

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

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NVIDIA AI Infrastructure Sample Questions (Q67-Q72):

NEW QUESTION # 67
You are evaluating the integration of NVIDIA BlueField DPUs into your data center's storage architecture to optimize AI workloads. The storage solution chosen has incorporated BlueField DPUs to enhance performance and efficiency. Which of the following benefits directly results from this integration?

A. Elimination of latency issues in data processing tasks.
B. Unlimited scalability by adding more DPUs without architectural changes.
C. Reduced CPU load by offloading data processing tasks to DPUs.
D. Enhanced I/O performance with NVMe storage access speeds.

Answer: C

Explanation:
NVIDIA BlueField DPUs improve storage architecture efficiency by offloading infrastructure and data-processing tasks from the host CPU. This frees CPU resources for AI workloads while the DPU handles storage, networking, and security-related processing more efficiently.

NEW QUESTION # 68
After a firmware upgrade on a DGX H100, the administrator notices that one GPU is not detected by the system. Which troubleshooting step should be performed first to identify the root cause?

A. Review firmware update logs and run nvsm show health to check for hardware or firmware errors on the affected GPU.
B. Immediately re-run the firmware upgrade on all system components.
C. Remove the GPU from the system and replace it with a new one before any diagnostics.
D. Ignore the issue and proceed with production workloads if the other GPUs are operational.

Answer: A

Explanation:
After a firmware upgrade, the first step is to check the upgrade logs and run nvsm show health to identify whether the missing GPU is related to firmware activation, hardware detection, PCIe issues, or a component fault. This provides diagnostic evidence before rerunning updates or replacing hardware.

NEW QUESTION # 69
Which of the following are key benefits of using NVIDIA NVLink Switch in a multi-GPU server setup for AI and deep learning workloads?

A. Enhanced security features compared to PCle based interconnections.
B. Increased GPU-to-GPIJ communication bandwidth.
C. Simplified GPU resource management.
D. Reduced latency in inter-GPU data transfers.
E. Support for larger GPU memory pools than a single server can physically accommodate.

Answer: B,D,E

Explanation:
NVLink provides significantly higher bandwidth and lower latency compared to PCle, enabling faster communication between GPUs. NVLink switches allow for pooling of GPU memory across multiple servers, enabling training of larger models that wouldn't fit on a single server's GPU memory. While it enhances infrastructure capabilities, it doesn't inherently simplify GPU resource management or directly provide enhanced security compared to PCle regarding the data transfer. Management features might exist on top of the NVLink infrastructure.

NEW QUESTION # 70
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. The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
B. Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.
C. Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.
D. Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.
E. The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).

Answer: A,B,C,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 # 71
A system administrator needs to configure a BlueField DPU and enable RShim on the baseboard management controller (BMC). Which command should be executed?

A. systemctl restart rshim
B. systemctl enable bmc-rshim.service
C. ipmitool raw 0x32 0x6a 1
D. scp <path_to_bfb> root@<bmc_ip>:/dev/rshim0/boot</bmc_ip></path_to_bfb>

Answer: C

Explanation:
The ipmitool raw 0x32 0x6a 1 command enables RShim access through the BMC for BlueField DPU configuration. This allows the BMC to expose the RShim interface needed for DPU provisioning and management.

NEW QUESTION # 72
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