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2025 Valid NVIDIA NCP-AII: NVIDIA AI Infrastructure Training Kit

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NVIDIA AI Infrastructure Sample Questions (Q16-Q21):

NEW QUESTION # 16
Consider the following *iptables' rule used in an A1 inference server. What is its primary function?
iptables -A INPUT -p tcp --dport 8080 -j ACCEPT

A. Drops all IJDP traffic destined for port 8080.
B. Blocks all TCP traffic on port 8080.
C. Redirects TCP traffic from port 8080 to another port.
D. Accepts all TCP traffic originating from port 8080.
E. Accepts all TCP traffic destined for port 8080.

Answer: E

Explanation:
The *iptables* rule 'iptables -A INPUT -p tcp -dport 8080 -j ACCEPT adds a rule to the INPUT chain, specifying that TCP traffic destined for port 8080 should be accepted. "-dport' specifies the destination port, and '-j ACCEPT indicates that the traffic should be allowed to pass through.

NEW QUESTION # 17
You are troubleshooting a performance issue with a GPU-accelerated application running inside a Docker container. The 'nvidia-smi' output inside the container shows the GPU is being utilized, but the performance is significantly lower than expected. Which of the following could be the cause of this performance bottleneck?

A. The version of the CUDA driver on the host is incompatible with the CUDA toolkit version used in the container.
B. The application is performing frequent small memory transfers between the CPU and GPIJ.
C. The host machine's CPU is being heavily utilized, causing a bottleneck in data transfer to the GPU.
D. The Docker container is not configured to use shared memory for data transfer with the GPU.
E. The GPU is overheating, causing thermal throttling.

Answer: A,B,C,E

Explanation:
Several factors could contribute to reduced GPU performance within a Docker container, even if the GPU is being utilized. A heavily loaded CPU (A) can bottleneck data transfer to the GPU. Incompatible CUDA driver versions between host and container (C) cause unexpected errors, and CUDA drivers are important for GPU support. Frequent small memory transfers between CPU and GPU (D) can be inefficient. Overheating (E) can cause the GPU to throttle its performance. While shared memory optimization (B) can help, it's not always the primary cause of the initial performance drop.

NEW QUESTION # 18
Which of the following techniques can be used to optimize storage performance for deep learning training?

A. Data deduplication
B. Data sharding
C. Using a larger block size for the file system
D. Data prefetching
E. Data compression using lossless algorithms (e.g., gzip)

Answer: B,C,D

Explanation:
Data prefetching anticipates future data needs and loads data into the cache before it is requested. A larger block size can improve I/O throughput for large files. Data sharding distributes data across multiple storage devices to increase parallelism. Data compression, while saving space, can add overhead during training. Data deduplication is not normally usefull for training data sets.

NEW QUESTION # 19
Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. Which strategy or tool would be least effective in mitigating these memory allocation stalls?

A. Optimize the model using TensorRT.
B. Enabling CUDA graph capture to reduce kernel launch overhead.
C. Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests.
D. Reducing the model's memory footprint by using quantization or pruning techniques.
E. Increasing the GPU's TCC (Tesla Compute Cluster) mode priority.

Answer: E

Explanation:
CUDA memory pools directly address memory allocation overhead. CUDA graph capture reduces kernel launch overhead, which can indirectly reduce memory pressure. Model quantization/pruning reduces the overall memory footprint. Optimizing using TensorRT reduces memory footprint. Increasing TCC priority primarily affects preemption behavior and doesn't directly address memory allocation issues. Therefore it will have less impact than others.

NEW QUESTION # 20
Which of the following statements regarding the benefits of using a BlueField DPU for network offload are TRUE? (Select TWO)

A. Simplified network configuration compared to traditional NICs.
B. Automatic compatibility with all existing network protocols without requiring software updates.
C. Reduced CPU utilization on the host server for network-related tasks.
D. Increased network throughput due to hardware acceleration.
E. Elimination of the need for a dedicated network interface card (NIC).

Answer: C,D

Explanation:
BlueField DPUs offload network tasks, freeing up CPU resources and improving network performance through hardware acceleration. Simplified configuration is not always guaranteed, as DPUs often require more configuration due to their advanced features. DPUs do not eliminate the need for a NIC, they are a NIC with advanced processing capabilities.

NEW QUESTION # 21
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