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100% Pass NVIDIA - NCP-AII - Latest Exam NVIDIA AI Infrastructure Quiz

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NVIDIA AI Infrastructure Sample Questions (Q237-Q242):

NEW QUESTION # 237
Consider a scenario where you need to run two different deep learning models, Model A and Model B, within separate Docker containers on the same NVIDIA GPU. Model A requires CUDA 11.2, while Model B requires CUDA 11.6. How can you achieve this while minimizing conflicts and ensuring each model has access to its required CUDA version?

A. Install both CUDA 11.2 and CUDA 11.6 inside each Docker container and use 'LD LIBRARY PATH' to switch between the CUDA versions for each model.
B. Mount the CUDA libraries from the host machine into both containers using Docker volumes, ensuring each container has access to both CUDA versions.
C. Use separate Docker images for each model, each based on the appropriate 'nvidia/cuda' image (e.g., 'nvidia/cuda:ll .2-base-ubuntu20.04' and nvidia/cuda: 1 1.6-base-ubuntu20.04 s).
D. Install both CUDA 11.2 and CUDA 11.6 on the host system and use 'CUDA VISIBLE DEVICES* to isolate each model to a specific CUDA version.
E. Create a single Docker image with both CUDA versions and dynamically link the correct CUDA libraries at runtime using environment variables.

Answer: C

Explanation:
The recommended and most straightforward approach is to use separate Docker images (B), each based on the specific nvidia/cuda' image version needed. This creates isolated environments, avoiding conflicts and ensuring each model has the correct CUDA toolkit. Installing multiple CUDA versions on the host (A) can lead to conflicts and isn't necessary with Docker. Installing multiple CUDA versions within a single container (C, D) adds complexity and potential conflicts. Mounting CUDA libraries from the host (E) might work, but it's less isolated and can create dependency management issues.

NEW QUESTION # 238
A data scientist reports slow data loading times when training a large language model. The data is stored in a Ceph cluster. You suspect the client-side caching is not properly configured. Which Ceph configuration parameter(s) should you investigate and potentially adjust to improve data loading performance? Select all that apply.

A. client quota
B. mds cache size
C. fuse_client_max_background
D. client cache size

Answer: C,D

Explanation:
Client-side caching in Ceph is primarily controlled by 'client cache size' which determines the amount of memory the Ceph client uses for caching data. 'mds cache size' controls the metadata server cache size, impacting metadata operations. controls the maximum number of background requests a FUSE client can make, influencing concurrency. affects the number of threads used by the OSDs, not the client-side caching, and 'client quota' limits storage usage, not caching.

NEW QUESTION # 239
You are running a Docker container with GPU support using 'nvidia-docker run'. The containerized application unexpectedly fails to detect the GPU. What is the most likely cause?

A. The NVIDIA drivers are not installed on the host system.
B. The =gpus all' flag was not specified when running the container.
C. The application within the container is not linked against the CUDA libraries.
D. The Docker daemon is not configured to use the NVIDIA runtime.
E. The Docker image does not include the CUDA toolkit.

Answer: B

Explanation:
When running containers that need GPU access, it's essential to explicitly request the GPU resources. The '-gpus all' or '-gpus device=..: flag passed to 'docker run' with the NVIDIA runtime allows the container access to the available GPUs. Without this flag, the container operates as if no GPUs are available. Options A, B, C and D, while potentially problematic, are not the most likely cause if 'nvidia-docker run' was used previously.

NEW QUESTION # 240
You're optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You've deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.
What are potential bottlenecks and optimization strategies? (Select all that apply)

A. Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.
B. The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
C. Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.
D. The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).
E. Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.

Answer: A,B,D,E

Explanation:
Multiple factors can contribute to low throughput in inference serving. Model loading overhead is significant, and dynamic batching is crucial to maximize throughput. Insufficient CPU cores and memory constraints on the GPU also limit performance. Model precision reduction helps reduce memory footprint and increase throughput. While PCle bandwidth is a factor, it is often not the primary bottleneck in inference serving.

NEW QUESTION # 241
Consider a scenario where you need to isolate GPU workloads in a multi-tenant Kubernetes cluster. Which of the following Kubernetes constructs would be MOST suitable for achieving strong isolation at both the resource and network level?

A. Using node affinity only.
B. Using namespaces with resource quotas and network policies.
C. Using pod affinity and anti-affinity rules to control pod placement.
D. Using labels and selectors to schedule workloads on specific GPU nodes.
E. Using taints and tolerations to dedicate GPU nodes to specific workloads.

Answer: B

Explanation:
Namespaces provide logical isolation within a Kubernetes cluster. Resource quotas limit the resources (including GPIJs) that a namespace can consume, while network policies control network traffic between namespaces, ensuring strong isolation. Options B, C, D, and E provide some level of control over pod placement but do not offer the same level of resource and network isolation as namespaces with resource quotas and network policies.

NEW QUESTION # 242
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