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NVIDIA AI Infrastructure Sample Questions (Q162-Q167):

NEW QUESTION # 162
You need to verify the integrity of the BlueField OS image before flashing it to the SmartNIC. Which method would provide the strongest guarantee that the image has not been tampered with?

A. Verifying the SHA256 checksum of the image against the published SHA256 checksum.
B. Checking the MD5 checksum of the image against the published MD5 checksum.
C. Utilizing gpg to verify the image.
D. Comparing the image size with the expected size from the download source.
E. Downloading the image from a trusted source.

Answer: A,C

Explanation:
SHA256 provides a stronger collision resistance than MD5, making it a more reliable method for verifying image integrity. Comparing the image size is not a reliable method, as a small change can be made without significantly altering the size. Downloading from a trusted source is important, but doesn't guarantee the image hasn't been tampered with after it was published. GPG provides cryptographic assurance that the file originated from and was signed by an identified party.

NEW QUESTION # 163
You're profiling the performance of a PyTorch model running on an AMD server with multiple NVIDIA GPUs. You notice significant overhead in the data loading pipeline. Which of the following strategies can help optimize data loading and improve GPU utilization?
Select all that apply.

A. Implementing asynchronous data prefetching using 'torch .Generator'.
B. Loading the entire dataset into RAM before training.
C. Using the 'torch.utils.data.DataLoader' with multiple worker processes.
D. IJsing a faster storage system (e.g., NVMe SSD instead of HDD).
E. Reducing the batch size to decrease the amount of data loaded per iteration.

Answer: A,C,D

Explanation:
Using multiple worker processes in 'DataLoader' enables parallel data loading. Asynchronous prefetching allows data to be loaded while the GPU is processing the current batch. A faster storage system reduces the 1/0 bottleneck. Loading the entire dataset into RAM might not be feasible for large datasets. Reducing batch size reduces the amount of data loaded but could decrease overall GPU utilizatiom

NEW QUESTION # 164
You are troubleshooting performance issues in an A1 training clusten You suspect network congestion. Which of the following network monitoring tools would be MOST helpful in identifying the source of the congestion?

A. iPerf/Netperf
B. tcpdump/Wireshark
C. netstat
D. Traceroute
E. Ping

Answer: A,B

Explanation:
iPerf/Netperf can measure network bandwidth and latency between nodes, allowing you to quantify congestion. tcpdump/Wireshark can capture and analyze network packets, providing detailed insights into traffic patterns and potential bottlenecks. Ping and Traceroute are useful for basic connectivity testing but not for detailed congestion analysis. netstat shows network statistics but doesn't capture packet-level details.

NEW QUESTION # 165
Consider this scenario. You have a containerized A1 application that requires specific CUDA libraries. You want to manage the deployment and scaling of this application across multiple Kubernetes clusters, some of which might have different versions of NVIDIA drivers installed. How would you handle the CUDA dependency management in this multi-cluster environment to ensure compatibility and reproducibility?

A. Package the CUDA libraries directly into the application container image, ensuring that each cluster uses the same version.
B. Use the 'latest' tag when pulling your container image to ensure the most up-to-date version is used.
C. Use a multi-stage Docker build to dynamically download and install the appropriate CUDA libraries based on the driver version detected on each cluster.
D. Leverage an init container to install the correct CUDA libraries before the application container starts, based on the detected driver version.
E. Build a different container image for each Kubernetes cluster, specifically tailored to the driver version installed on that cluster.

Answer: A

Explanation:
Packaging the CUDA libraries into the container image ensures consistent behavior across all clusters, regardless of the driver version installed on each cluster's nodes. It is the most portable solution. Options B and C introduce runtime dependencies and increase complexity. Option D becomes unwieldy to manage. Option E is highly discouraged as the image can be updated without your knowledge.

NEW QUESTION # 166
A GPU in your AI server consistently overheats during inference workloads. You've ruled out inadequate cooling and software bugs.
Running 'nvidia-smi' shows high power draw even when idle. Which of the following hardware issues are the most likely causes?

A. Degraded thermal paste between the GPU die and the heatsink.
B. A BIOS setting that is overvolting the GPU.
C. A failing voltage regulator module (VRM) on the GPU board, causing excessive power leakage.
D. Incorrectly seated GPU in the PCle slot, leading to poor power delivery.
E. Insufficient system RAM.

Answer: A,C,D

Explanation:
Degraded thermal paste loses its ability to conduct heat effectively. A failing VRM can cause excessive power draw and heat generation. An incorrectly seated GPU can cause instability and poor power delivery, leading to overheating. Overvolting in BIOS will definitely cause overheating. While insufficient RAM can cause performance issues, it is less likely to lead to overheating.

NEW QUESTION # 167
......

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NVIDIA AI Infrastructure Online Questions - Outstanding Practice To your NCP-AII Exam

NVIDIA NCP-AII Exam Questions [2025]

NVIDIA AI Infrastructure Sample Questions (Q162-Q167):

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