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NVIDIA AI Operations Sample Questions (Q34-Q39):

NEW QUESTION # 34
When deploying BCM using Helm, which Helm chart parameter controls the resource limits for the BCM pods (CPU and Memory)?

A. 'bcmResources.limits'
B. 'deployment.resources.limits'
C. 'resources.bcmLimits'
D. resources.limits'
E. bcm.resources.limits'

Answer: D

Explanation:
The 'resources.limits' parameter in the Helm chart is the standard way to specify resource limits (CPU and memory) for pods deployed via Helm. This parameter is used to configure the resource requests and limits for the BCM pods.

NEW QUESTION # 35
Your Kubernetes cluster hosts several AI workloads with varying GPU requirements. Some workloads require high compute performance, while others are memory-bound. You want to optimize GPU resource allocation by bin-packing workloads with complementary resource profiles onto the same nodes. How would you approach this?

A. Implement a vertical pod autoscaler (VPA) to dynamically adjust the GPU resource requests of pods based on their actual usage.
B. Employ a custom Kubernetes scheduler that considers GPU compute and memory usage when placing pods.
C. Manually assign workloads to specific nodes based on their resource requirements.
D. Configure resource quotas to limit the overall GPU usage in the cluster.
E. Use node affinity rules to schedule all workloads onto the same set of nodes, regardless of their resource profiles.

Answer: B

Explanation:
The correct answer is B. A custom Kubernetes scheduler allows you to implement sophisticated bin-packing algorithms that consider both GPU compute and memory usage to optimize resource allocation. This enables you to pack workloads with complementary resource profiles onto the same nodes, maximizing GPU utilization. Option A ignores resource profiles. Option C is not scalable. Option D limits overall usage but doesn't optimize placement. Option E focuses on individual pod resource adjustment, not bin-packing.

NEW QUESTION # 36
A Kubernetes pod running a GPU-accelerated application is failing to start. The pod's logs show the error message 'Failed to initialize NVML: Driver/library version mismatch'. What are the MOST likely causes of this issue?

A. The Docker daemon is not running.
B. The 'nvidia-container-runtime' is not properly configured on the Kubernetes nodes.
C. The Kubernetes version is incompatible with the NVIDIA drivers.
D. The NVIDIA driver version on the host is incompatible with the CUDA version used in the container image.
E. The GPU is not properly seated in the server.

Answer: B,D

Explanation:
A 'Driver/library version mismatch' error strongly suggests an incompatibility between the host's NVIDIA driver and the CUDA libraries in the container. Improper configuration of the 'nvidia-container-runtime' (which handles GPU access for containers) is also a likely cause. While a loose GPU (C) would prevent GPU access altogether, it's less likely to cause this specific error. The Docker daemon (D) is essential for containers, but this specific error points to NVIDIA-related issues. Kubernetes version incompatibility (E) is possible, but less common than driver/CUDA mismatch.

NEW QUESTION # 37
You are experiencing performance issues with a specific AI workload running on your Kubernetes cluster managed by BCM. BCM shows high GPU utilization for this workload. How can you use BCM to further investigate the cause of the performance bottleneck?

A. Use BCM to monitor the network bandwidth between the GPU nodes and the storage system.
B. Use BCM to profile the workload's GPU usage and identify specific kernels or operations that are consuming the most GPU time.
C. Use BCM to adjust the clock speeds of the GPUs to maximize performance for this workload.
D. Use BCM to restart the Docker container running the workload.
E. Use BCM to migrate the workload to a different GPU node with more available resources.

Answer: A,B

Explanation:
BCM's integration with profiling tools allows you to analyze the workload's GPU usage and identify performance bottlenecks. You can also monitor the network bandwidth, as data transfer bottlenecks can significantly impact AI workload performance. While migrating the workload might help, understanding the bottleneck first is crucial. Adjusting clock speeds can be risky. Restarting the container is a general troubleshooting step but doesn't provide specific insights.

NEW QUESTION # 38
A user reports slow performance when running a CUDA application within a Docker container. You suspect the container is not properly utilizing the GPU. How can you quickly verify that the container has access to the NVIDIA GPU?

A. Run 'nvidia-smr inside the container. If it shows GPU information, the container has access.
B. Check the Docker container logs for any NVIDIA-related error messages.
C. Execute 'docker inspect and look for the 'NVIDIA VISIBLE DEVICES environment variable.
D. Inspect the Dockerfile to ensure that the 'nvidia/cuda' base image or appropriate NVIDIA drivers are installed.
E. Restart the Docker daemon.

Answer: A,B,C

Explanation:
Running 'nvidia-smr inside the container (A) is the quickest way to verify GPU access. Checking container logs (B) can reveal errors related to GPU initialization. Inspecting the container (D) for 'NVIDIA VISIBLE DEVICES' shows which GPUs are exposed to the container. Inspecting the Dockerfile (C) is useful for understanding the image's configuration, but it doesn't confirm runtime access. Restarting Docker (E) might resolve transient issues, but it's not a diagnostic step.

NEW QUESTION # 39
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