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NVIDIA NCP-AII Dumps

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NVIDIA NCP-AII Sample Questions

Question # 1

What is the primary purpose of running an NCCL burn-in test on a new GPU cluster?

A. To test whether GPUs are properly detected by the operating system and have the correct drivers installed. 
B. To maximize GPU utilization for machine learning workloads and automatically tune deep learning frameworks. 
C. To detect and resolve hardware or interconnect issues before production by stressing GPU communication links. 
D. To benchmark application-specific runtime performance of AI models using real user data and production training scripts. 



Question # 2

After a recent OS upgrade, you need to reinstall NVIDIA GPU and DOCA drivers to support both AI training and accelerated networking. What best practice ensures successful installation and full hardware capability?

A. Download and install only the specific versions of GPU and DOCA drivers listed as compatible with the current OS and hardware. 
B. Apply legacy drivers for hardware released within the last two years to maintain maximum compatibility across versions. 
C. Install the latest available drivers directly from the NVIDIA website. 
D. Use the default drivers provided by the Linux distribution, unless an installation fails during system boot. 



Question # 3

A healthcare organization is deploying an AI system to analyze patient data for predictive diagnostics. The system must comply with strict data protection regulations such as HIPAA, ensuring that sensitive information remains confidential and secure. Considering the need for robust security measures, which combination of strategies should the organization prioritize to protect against data breaches and ensure regulatory compliance?

A. Deploy data masking to obscure sensitive data during processing and use role-based access control (RBAC) to limit data access based on user roles. 
B. Use tokenization to replace sensitive data with non-sensitive tokens and employ multifactor authentication (MFA) for system access. 
C. Implement symmetric encryption for all data at rest and rely solely on password-based access controls. 
D. Rely on asymmetric encryption for all communications and use data deduplication to minimize storage costs without additional security measures. 



Question # 4

An enterprise is deploying an AI Factory using NVIDIA DGX BasePOD architecture. The infrastructure team must ensure high availability and efficient data transfer between compute nodes. Which network topology should they implement for the InfiniBand fabric? 

A. Simple ring topology connecting all nodes in a loop. 
B. Fat-Tree topology with rail-optimized design. 
C. Single flat Ethernet network for all traffic. 
D. Star topology with all nodes connected to a single central switch. 



Question # 5

An administrator needs to perform a comprehensive pre-production stress test on a DGX H100 system. Which command validates GPU, CPU, memory, and storage components while following NVIDIA’s recommended procedure?

A. nvidia-smi -q | grep "GPU Stress Test" 
B. sudo nvsm stress-test --force 
C. stress --cpu $(nproc) --io $(nproc) --timeout 600 
D. ./gpu_burn 60 



Question # 6

A DGX server reports degraded performance and storage alerts. How would you use NVSM and nvidia-smi to troubleshoot both system and GPU issues?

A. Use nvsm show health for a system health summary, nvsm show storage for storage issues, and nvidia-smi -q to get detailed GPU information. 
B. Run nvsm collect-stats to gather logs, use lsblk to understand if there are storage problems, and nvidia-smi -q to get detailed GPU information. 
C. Start by issuing nvidia-smi -L to list GPUs, followed by nvsm --refresh to clear all alerts, and nvidia-smi -q to get detailed GPU information. 
D. Run nvsm reset to restore system health, then use nvidia-smi --fix for automatic GPU repairs and status recovery.



Question # 7

An infrastructure engineer runs an NCCL burn-in on an eight-node GPU cluster. Over a 12- hour period, all GPUs are tested with repeated all-reduce collectives. Monitoring tools show the following observations: Aggregate bandwidth remains within 5% of documented reference for the hardware on every run. No errors or timeouts are reported in NCCL logs. On three occasions, one GPU logged single-run bandwidth dips of 15–20% compared to its normal performance, but performance recovered on the next run and stayed stable afterward. System logs show no hardware or driver errors. Two minor NCCL WARN-level messages about “unexpected latency spike” appear in system logs for separate nodes, but could not be reproduced. Which conclusion is the best strategy before releasing the cluster to production?

A. Proceed, since all bandwidth targets are met, issues were transient and self-resolved, and there are no persistent errors or timeouts across repeated burn-ins. 
B. Recommend proactive maintenance, because any bandwidth drop, even if transient and unreproducible, shows the burn-in failed; clusters must not show performance variance above 10% for any GPU even once. 
C. Approve for AI workload use, but flag affected nodes for manual exclusion from distributed training jobs, as nodes showing any anomaly should be isolated whenever possible. 



Question # 8

An infrastructure engineer is preparing a new AI cluster for production use, relying on NVIDIA switches and high-speed optical transceivers for node connectivity. The team is finalizing network validation before launching large-scale training jobs. Why is it critical to confirm and align the firmware version on all switch transceivers prior to production?

A. To guarantee that hardware inventory tools can report serial numbers and manufacturer codes for asset management, which is critical for future support and troubleshooting. 
B. To ensure stability, bandwidth, and compatibility across the cluster, avoiding link issues and performance loss. 
C. To allow the network operating system to automatically discover all connected transceivers with heterogeneous firmware. 
D. To reduce GPU memory consumption during distributed training jobs. 



Question # 9

Which statement best explains why maintaining high cable signal quality is essential in modern high-speed data centers?

A. High cable signal quality ensures that cable length and connector type do not play as big a role in deploying new infrastructure in the data center. 
B. High cable signal quality minimizes bit error rates and supports reliable, high-throughput communication, reducing retransmissions and congestion across the network. 
C. High cable signal quality reduces electromagnetic interference (EMI) and crosstalk, helping prevent unexpected packet drops during sustained workloads. 
D. High cable signal quality enables effective use of Forward Error Correction (FEC), which is required for reliable operation at high data rates such as 200GbE and above. 



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