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NVIDIA AI Operations Sample Questions (Q276-Q281):

NEW QUESTION # 276
When deploying a VMI container that utilizes CUDA, what is the primary purpose of the NVIDIA Container Toolkit?

A. To automatically scale the number of VMI containers based on workload.
B. To automatically install the correct NVIDIA drivers on the host system.
C. To manage and orchestrate Docker containers across multiple hosts.
D. To provide CUDA libraries and drivers inside the container, enabling GPU acceleration.
E. To monitor the GPU utilization of the container in real-time.

Answer: D

Explanation:
The NVIDIA Container Toolkit allows you to build and run GPU-accelerated containers by providing the necessary CUDA libraries and drivers inside the container, ensuring that the application can leverage the GPU.

NEW QUESTION # 277
You are using NVSHMEM to manage shared memory across multiple GPUs in a multi-node cluster. Your application is crashing with out- of-memory errors, even though the reported GPU memory usage is well below the total available. You have already confirmed sufficient physical RAM on all nodes. What is the MOST likely cause, related to NVSHMEM configuration, of these out-of-memory errors?

A. The InfiniBand drivers are outdated.
B. The CUDA driver version is incompatible with NVSHMEM.
C. The NCCL library is not properly initialized.
D. The 'NVSHMEM SYMMETRIC SIZE environment variable is set too low.
E. 'CUDA_VISIBLE DEVICES is set incorrectly

Answer: D

Explanation:
The 'NVSHMEM SYMMETRIC SIZE environment variable defines the total amount of shared memory available to NVSHMEM across all nodes. If this value is too small, even if individual GPUs have sufficient memory, the overall NVSHMEM shared memory pool may be exhausted, leading to out-of-memory errors. CUDA driver incompatibility, NCCL issues, and outdated InfiniBand drivers could cause other problems, but they are less likely to directly cause out-of-memory errors when individual GPU usage is low . CUDA_VISIBLE_DEVICES may effect device enumeration and memory allocation, but is the master environment variable.

NEW QUESTION # 278
You are managing a large Slurm cluster used for AI research. You notice that some users are submitting jobs that request excessive amounts of memory, even though their applications don't actually need it, leading to inefficient resource utilization. What steps can you take to address this issue and encourage users to request more appropriate memory resources?

A. Educate users on how to accurately estimate their memory requirements and provide tools for monitoring memory usage.
B. Adjust the scheduler's priority algorithm to penalize jobs that request excessive memory.
C. All of the above
D. Configure Slurm's accounting system to track memory usage and provide reports to users, highlighting inefficient memory requests.
E. Implement strict memory limits per job and automatically kill jobs that exceed those limits.

Answer: C

Explanation:
A multi-faceted approach is needed. Enforcing limits (A) prevents waste, education (B) helps users improve requests, accounting (C) provides feedback, and adjusting priority (D) discourages wasteful requests. A, B, C, D combined provide the best approach.

NEW QUESTION # 279
After installing Kubernetes on your NVIDIA hosts using BCM, you notice that the GPU metrics are not being collected by your monitoring system (e.g., Prometheus). You've confirmed that the NVIDIA Device Plugin is running correctly and GPUs are accessible to containers.
What is the next MOST likely component to investigate and how would you address it?

A. The NVIDIA Data Center GPU Manager (DCGM) exporter is not deployed or configured correctly. Deploy and configure the DCGM exporter to expose GPU metrics in a Prometheus-compatible format.
B. The cluster's logging driver is interfering with metrics collection. Switch to a different logging driver (e.g., journald) that doesn't conflict with metrics collection.
C. The Kubernetes API server is throttling metrics requests. Increase the API server's throttling limits for metrics requests.
D. The Prometheus service discovery is not configured to scrape metrics from the NVIDIA Device Plugin endpoint. Update the Prometheus configuration to include the device plugin's metrics endpoint.
E. The kubelet's resource usage metrics endpoint is not properly configured. Edit the kubelet configuration file to enable GPU metrics collection.

Answer: A

Explanation:
The NVIDIA Data Center GPU Manager (DCGM) exporter is specifically designed to collect and expose GPU metrics in a format that Prometheus can consume. If GPU metrics are not being collected, the DCGM exporter is the most likely culprit. The other options are less directly related to GPU metric collection. Option A pertains more to core Kubernetes metrics, option C relates to generic prometheus service discovery which isn't specialized to GPU data. Logging drivers and API throttling are less likely to directly block metrics collection.

NEW QUESTION # 280
You are deploying a containerized AI application from NGC on a cluster with multiple GPU nodes. You want to ensure that the application is distributed across multiple GPUs and nodes for maximum performance. What strategies can you employ to achieve this?

A. Utilize a distributed training framework like Horovod or DeepSpeed to distribute the workload across multiple GPUs and nodes.
B. Use a single container with multi-GPU support and configure it to utilize all available GPUs on the cluster.
C. Configure Kubernetes resource quotas to limit the number of GPUs available to each container.
D. Deploy multiple replicas of the container, each configured to use a specific subset of GPUs on different nodes.
E. Employ a message queue system like RabbitMQ or Kafka to distribute data to the containers running on different nodes.

Answer: A,D,E

Explanation:
B, C, and E are correct. Deploying multiple container replicas allows for distribution across nodes. Distributed training frameworks manage workload distribution. A message queue facilitates data distribution to different nodes. A is incorrect as it relies on a single container handling all GPUs. D is used for resource management, not distribution.

NEW QUESTION # 281
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