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NVIDIA AI Operations Sample Questions (Q39-Q44):
NEW QUESTION # 39 
A BCM pipeline deployed using containers is failing to launch with a 'CUDA driver version is insufficient for CUDA runtime version' error.
What is the root cause and solution?
- A. The 'nvidia-container-runtime' is not properly configured. Verify the configuration and restart the container runtime.
 - B. The host machine's NVIDIA driver is older than the CUDA runtime version used in the container. Upgrade the host's NVIDIA driver.
 - C. The container image has an incorrect CUDA runtime version. Rebuild the container image with a CUDA runtime version compatible with the host driver.
 - D. A and B
 - E. The container is not requesting GPU resources correctly. Ensure the container runtime is configured to allow GPU access.
 
Answer: D
Explanation:
The error indicates a mismatch between host driver and container runtime versions. Upgrading the driver or rebuilding the container with a compatible runtime are the solutions.
NEW QUESTION # 40 
A system administrator is troubleshooting a Docker container that crashes unexpectedly due to a segmentation fault. They want to generate and analyze core dumps to identify the root cause of the crash.
Why would generating core dumps be a critical step in troubleshooting this issue?
- A. Core dumps restore the process to its previous state, often fixing the error-causing crash.
 - B. Core dumps capture the memory state of the process at the time of the crash.
 - C. Core dumps prevent future crashes by stopping any further execution of the faulty process.
 - D. Core dumps provide real-time logs that can be used to monitor ongoing application performance.
 
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Core dumps capture thememory state of a process at the time of its crash, providing a snapshot useful for post- mortem debugging. Analyzing core dumps helps identify the cause of segmentation faults or other critical errors by revealing what the process was doing at failure, including stack traces, variable states, and memory content.
NEW QUESTION # 41 
A system administrator needs to configure and manage multiple installations of NVIDIA hardware ranging from single DGX BasePOD to SuperPOD.
Which software stack should be used?
- A. Base Command Manager
 - B. NetQ
 - C. Magnum IO
 - D. Fleet Command
 
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA's Base Command Manager is the software stack designed specifically for configuration, management, and monitoring of NVIDIA DGX systems, from a single DGX BasePOD up to large-scale SuperPOD deployments. It provides centralized management capabilities to orchestrate AI infrastructure, simplifying deployment, hardware monitoring, and lifecycle management across multiple clusters and data centers.
* NetQ is focused on network monitoring and diagnostics rather than overall hardware cluster management.
* Fleet Command is an enterprise SaaS solution to deploy and manage AI infrastructure in hybrid cloud environments but is not specifically targeted at on-premises DGX BasePOD to SuperPOD scale hardware management.
* Magnum IO is NVIDIA's high-performance data and storage software stack for managing I/O but not hardware or cluster configuration management.
Therefore, Base Command Manager is the correct and dedicated tool for managing multiple installations of NVIDIA DGX hardware spanning from BasePOD to SuperPOD environments.
This is consistent with NVIDIA's official AI Operations documentation and product descriptions highlighting Base Command Manager as the unified command and control platform for AI infrastructure management.
NEW QUESTION # 42 
You suspect that 'nvsm' is not properly utilizing all available resources on your system. What metrics can you monitor to assess the resource consumption of 'nvsm' and identify potential bottlenecks?
- A. System uptime and kernel version.
 - B. Disk I/O and filesystem latency.
 - C. GPU utilization and memory usage.
 - D. CPU usage, memory usage, and network 1/0.
 - E. Power consumption of the NVSwitch.
 
Answer: D
Explanation:
To assess the resource utilization of 'nvsm' , you should monitor its CPU usage, memory usage, and network I/O. These metrics will provide insights into potential bottlenecks and areas where the service might be constrained.
NEW QUESTION # 43 
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. Use a single container with multi-GPU support and configure it to utilize all available GPUs on the cluster.
 - B. Configure Kubernetes resource quotas to limit the number of GPUs available to each container.
 - C. Utilize a distributed training framework like Horovod or DeepSpeed to distribute the workload across multiple GPUs and nodes.
 - D. Employ a message queue system like RabbitMQ or Kafka to distribute data to the containers running on different nodes.
 - E. Deploy multiple replicas of the container, each configured to use a specific subset of GPUs on different nodes.
 
Answer: C,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 # 44
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