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NVIDIA AI Infrastructure Sample Questions (Q69-Q74):

NEW QUESTION # 69
A server with four installed NVIDIA GPUs is experiencing intermittent crashes during heavy AI training workloads. You suspect a power issue. You have monitored the power consumption and found that the GPUs are briefly exceeding the rated power capacity of the PSU during peak loads. What are TWO effective mitigation strategies you can implement? (Select TWO)

A. Re-seat the GPUs in their respective slots.
B. Replace the PSU with a higher wattage PSU.
C. Underclock the GPIJs to reduce their power consumption.
D. Increase the server room temperature.
E. Disable one of the GPUs to reduce the total power draw.

Answer: B,C

Explanation:
Underclocking the GPUs reduces their power consumption directly. Replacing the PSU provides more headroom to handle the peak loads. Disabling a GPU reduces performance. Increasing server room temperature exacerbates the problem. Reseating GPUs addresses connection issues, not power limitations.

NEW QUESTION # 70
You are tasked with optimizing an Intel Xeon scalable processor-based server running a TensorFlow model with multiple NVIDIA GPUs.
You observe that the CPU utilization is low, but the GPU utilization is also not optimal. The profiler shows significant time spent in 'tf.data' operations. Which of the following actions would MOST likely improve performance?

A. Use 'tf.data.AUTOTIJNE to allow TensorFlow to dynamically optimize the data pipeline.
B. Increase the number of threads used for CPU-bound operations in TensorFlow using 'tf.config.threading.set_intra_op_parallelism_threads()'.
C. Upgrade the server's network adapter to a faster interface, such as 100Gb
D. Reduce the global batch size to improve memory utilization.
E. Enable XLA (Accelerated Linear Algebra) compilation in TensorFlow.

Answer: A

Explanation:
'tf.data' performance issues often stem from inefficient data pipelines. 'tf.data.AIJTOTUNE allows TensorFlow to dynamically optimize the pipeline by adjusting parameters such as prefetch buffer size and the number of parallel calls to transformation functions. XLA compilation optimizes graph execution, but 'tf.data' issues need to be addressed first. Increasing CPU threads might help but 'AUTOTUNE is more specific to the problem. A smaller batch size could negatively impact GPU utilization. Network upgrades are irrelevant as the problem lies within the server.

NEW QUESTION # 71
You have a GPU-intensive application that requires the latest features of CUDA 12. However, your host system's NVIDIA driver is only compatible with CUDA 11.8. What steps can you take to enable your application to use CUDA 12 within a Docker container, without upgrading the host driver?

A. Downgrade the application to use CUDA 11.8 to match the host's driver version.
B. Upgrade the NVIDIA driver on the host system to the latest version compatible with CUDA 12.
C. Mount the CUDA 12 libraries from a separate Docker volume into the container and configure the accordingly.
D. Use a Docker image based on 'nvidia/cuda:12.0-base-ubuntu20.04'. The NVIDIA Container Toolkit will automatically handle the driver compatibility between the host and the container.
E. Install CUDA 12 inside the Docker container and set the 'CUDA DRIVER VERSION' environment variable to match the host driver version.

Answer: D

Explanation:
The NVIDIA Container Toolkit enables compatibility between the host driver and the CUDA version within the container (B). Using a Docker image based on will allow your application to leverage CUDA 12 features. Upgrading the host driver (A) is an option but not necessary and may introduce other compatibility issues. Setting (C) is not a standard or reliable approach. Mounting CUDA libraries from a volume (D) is complex and might not resolve driver version mismatches. Downgrading the application (E) avoids the problem but sacrifices access to CUDA 12 features. Because NVIDIA ensures a degree of backwards compatibility, the newer toolkit in the container can often work with an older driver on the host.

NEW QUESTION # 72
After successfully installing the NVIDIA Container Toolkit and configuring Docker, you're attempting to build a container image that leverages the GPU. You're using a Dockerfile but encounter the following error during the 'docker build' process: 'error during connect: this error may indicate that the docker daemon is not running'. However, the Docker daemon IS running. What is the most likely reason the build process is failing to connect, specifically in the context of GPU-enabled containers?

A. The user executing the 'docker builcf command does not belong to the 'docker' group.
B. The container requires more memory than the host is providing and the docker build command exited due to OOM.
C. The -gpus all' flag (or similar) needs to be passed to the 'docker build' command to enable GPU access during the build process, as it is needed for building images that require cuda.
D. The Docker daemon does not have sufficient permissions to access the NVIDIA GPUs.
E. The Docker daemon is configured to use a different networking driver than the one expected by the NVIDIA Container Toolkit.

Answer: C

Explanation:
The error 'error during connect: this error may indicate that the docker daemon is not running' during a 'docker build', when the daemon actually is running, can indicate a failure to connect to the daemon for a specific reason related to GPU access. 'docker build' requires - gpus all' to be passed in order for CUDA to correctly build the image. Permissions(A) are unlikely to cause this specific connection error. User group issues(B) are usually related to running containers, not building them. A networking issue (D) is possible but less likely in the context of a local build. The same can be said for container exceeding the hosts' available memory.

NEW QUESTION # 73
You are tasked with automating the BlueField OS deployment process across a large number of SmartNICs. Which of the following methods is MOST suitable for this task?

A. Utilizing a custom-built python script to flash each individual card, controlled from a central server. This method supports parallel flashing.
B. Utilizing the 'dd' command to directly copy the image to each SmartNIC's flash memory.
C. Manually flashing each SmartNIC using the 'bfboot utility on a workstation.
D. Creating a custom ISO image with the BlueField OS and booting each SmartNIC from a USB drive.
E. Using a network boot (PXE) server to deploy the BlueField OS image over the network. This allows centralized management and scalability.

Answer: E

Explanation:
PXE boot allows for automated and scalable OS deployment over the network, making it the most suitable option for managing a large number of SmartNlCs. Manually flashing or using USB drives is not practical at scale, and using 'dd' directly can be risky and error-prone without proper checks.

NEW QUESTION # 74
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New NCP-AII Test Pdf | New NCP-AII Dumps Pdf

New NCP-AII Dumps Pdf & NCP-AII Training Questions

NVIDIA AI Infrastructure Sample Questions (Q69-Q74):

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