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Latest Released NVIDIA NCP-AII Reliable Study Questions - NCP-AII Latest NVIDIA AI Infrastructure Exam Practice

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NVIDIA AI Infrastructure Sample Questions (Q232-Q237):

NEW QUESTION # 232
You're optimizing a deep learning model for deployment on NVIDIA Tensor Cores. The model uses a mix of FP32 and FP16 precision. During profiling with NVIDIA Nsight Systems, you observe that the Tensor Cores are underutilized. Which of the following strategies would MOST effectively improve Tensor Core utilization?

A. Enable CUDA graph capture to reduce kernel launch overhead.
B. Ensure that all matrix multiplications are performed using FP16 precision.
C. Pad the input tensors to dimensions that are multiples of 8 for optimal Tensor Core alignment.
D. Decrease the learning rate to improve training stability and reduce the need for gradient clipping.
E. Increase the batch size to fully utilize the available GPU memory.

Answer: C

Explanation:
Padding input tensors (C) to multiples of 8 is crucial for optimal Tensor Core performance, as Tensor Cores operate most efficiently on data with these dimensions. Using FP16 (B) is important, but proper alignment is key for full utilization. Increasing batch size (A) can improve overall throughput but doesn't directly address Tensor Core utilization. CIJDA graph capture (D) reduces kernel launch overhead, not Tensor Core utilization directly. Decreasing learning rate (E) is unrelated to Tensor Core performance.

NEW QUESTION # 233
Which of the following statements are true regarding the use of Congestion Management (CM) and Congestion Avoidance (CA) techniques within an InfiniBand fabric using NVIDIA technology? (Select TWO)

A. CM can include techniques like rate limiting to throttle traffic flows when congestion is detected.
B. CM/CA are not relevant in InfiniBand fabrics because InfiniBand's lossless nature guarantees that no packets will ever be dropped due to congestion.
C. CM aims to reduce the severity of congestion once it has already occurred, while CA aims to prevent congestion from happening in the first place.
D. InfiniBand's Explicit Congestion Notification (ECN) is a CA mechanism that allows switches to signal congestion to endpoints before packet loss occurs.
E. CM/CA mechanisms are primarily implemented at the IP layer and are independent of the InfiniBand transport layer.

Answer: A,C,D

Explanation:
CM and CA are crucial for maintaining performance in InfiniBand fabrics. CM addresses congestion that has already occurred, while CA tries to prevent it. ECN is a key CA mechanism in InfiniBand. InfiniBand is lossless but experiences performance degradation if congestion occurs; CM helps mitigate this. They are not implemented at the IP layer; they are integral parts of the InfiniBand transport. Rate limiting is a viable CM strategy.

NEW QUESTION # 234
Which command is used to verify the installation and configuration of the NGC CLI after initial setup?

A. 'ngc setup verify'
B. 'ngc system statue'
C. 'ngc -version'
D. 'ngc config validate'
E. 'ngc registry model list'

Answer: C,E

Explanation:
'ngc -version' confirms the CLI is installed and shows its version. 'ngc registry model list' verifies that the CLI can authenticate with NGC and access the registry.

NEW QUESTION # 235
You are using GPU Direct RDMA to enable fast data transfer between GPUs across multiple servers. You are experiencing performance degradation and suspect RDMA is not working correctly. How can you verify that GPU Direct RDMA is properly enabled and functioning?

A. Use the 'ibstat command to verify that the InfiniBand interfaces are active and connected.
B. Check the output of 'nvidia-smi topo -m' to ensure that the GPUs are connected via NVLink and have RDMA enabled.
C. Ping the other servers to ensure network connectivity.
D. Examine the 'cimesg' output for any errors related to RDMA or InfiniBand drivers.
E. Run a bandwidth benchmark using a tool like or to measure the RDMA throughput.

Answer: A,D,E

Explanation:
'dmesg' will show errors during RDMA driver initialization. Sibstat' confirms the InfiniBand interface status. Benchmarking with or validates the actual RDMA throughput. 'nvidia-smi topo -m' shows the topology but not necessarily active RDMA. Pinging only verifies basic network connectivity, not RDMA functionality.

NEW QUESTION # 236
You are deploying a multi-tenant AI infrastructure where different users or groups have isolated network environments using VXLAN. Which of the following is the MOST important consideration when configuring the VTEPs (VXLAN Tunnel Endpoints) on the hosts to ensure proper network isolation and performance?

A. Using the default MTU size of 1500 bytes for VXLAN traffic.
B. Ensuring that each tenant has a unique VXLAN Network Identifier (VNI) to isolate their traffic.
C. Using the same IP address for all VTEPs to simplify routing.
D. Disabling multicast routing to prevent broadcast traffic.
E. Using the same VNI for all tenants to maximize network utilization.

Answer: B

Explanation:
The most critical aspect of VXLAN configuration for multi-tenancy is ensuring that each tenant has a unique VNI. The VNI is a 24-bit identifier that segments the VXLAN network into isolated logical networks. Using unique VNIs guarantees that traffic from one tenant remains isolated from other tenants, preventing data leakage and ensuring security. Using overlapping or shared VNIs would defeat the purpose of VXLAN- based isolation.

NEW QUESTION # 237
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