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NVIDIA AI Operations Sample Questions (Q52-Q57):
NEW QUESTION # 52
You are monitoring the resource utilization of a DGX SuperPOD cluster using NVIDIA Base Command Manager (BCM). The system is experiencing slow performance, and you need to identify the cause.
What is the most effective way to monitor GPU usage across nodes?
- A. Check the job logs in Slurm for any errors related to resource requests.
- B. Use nvidia-smi on each node to monitor GPU utilization manually.
- C. Use the Base View dashboard to monitor GPU, CPU, and memory utilization in real-time.
- D. Run the top command on each node to check CPU and memory usage.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
TheBase View dashboardin NVIDIA Base Command Manager provides a centralized and real-time overview of GPU, CPU, and memory utilization across all nodes in the DGX SuperPOD cluster. This tool allows administrators to quickly identify bottlenecks and resource usage patterns efficiently, unlike manually checking logs or running commands node-by-node.
NEW QUESTION # 53
When deploying a DOCA application using the command, which of the following parameters are mandatory for specifying the DOCA core application's entry point?
- A. -num-cores Specifies the number of CPU cores to be allocated to the DOCA application.
- B. '-log-level ' : Sets the verbosity level for logging messages from the DOCA application.
- C. -entry-point Specifies the name of the function within the DOCA application that should be executed as the entry point.
- D. '-pci-address Defines the PCl address of the BlueField DPLJ where the application will run.
- E. '-class The DOCAApplication class in the DOCA executable
Answer: C,E
Explanation:
The zentry-point' parameter is required to specify the function that will be the entry point of the DOCA core application. '--class' parameter is mandatory.
NEW QUESTION # 54
In a data center designed for AI, what is the primary benefit of using GPU virtualization technologies like NVIDIA vGPU?
- A. To reduce the overall power consumption of the data center.
- B. To increase the number of physical GPUs that can be installed in a server.
- C. To eliminate the need for high-bandwidth networking.
- D. To simplify the deployment of AI applications on bare metal servers.
- E. To improve GPU utilization by allowing multiple virtual machines to share a single physical GPU.
Answer: E
Explanation:
GPU virtualization allows for better resource utilization by dividing a physical GPU among multiple VMs, improving efficiency and reducing costs. While power consumption can be indirectly affected by more efficient resource allocation, that's not the primary benefit.
NEW QUESTION # 55
You are troubleshooting a cluster with NVIDIA NVLink and NVSwitch. The fabric manager service ('nvsm') appears to be running, but the NVLink topology is not being discovered correctly. What is the FIRST step you should take to isolate the issue?
- A. Increase the logging level of 'nvsm' to DEBUG and restart the service.
- B. Immediately restart all GPUs in the system.
- C. Check the system's hardware for physical damage.
- D. Check the '/var/log/nvsm/nvsm.log' file for any error messages or warnings.
- E. Reinstall the NVIDIA drivers.
Answer: D
Explanation:
Checking the 'nvsm.log' file is the first and most logical step. Log files often contain valuable clues about errors or warnings related to the service's operation. Debug logging can be helpful, but it's best to start with the default logging level before increasing verbosity as high verbosity can make logs harder to parse. Other steps are more intrusive and should be done after reviewing the logs.
NEW QUESTION # 56
You have a Run.ai cluster integrated with NVIDIA's Cluster Manager (ACM). A data scientist reports that their job is being preempted frequently, even though they have a high-priority quot a. What are the MOST likely reasons for this preemption, assuming ACM is configured correctly?
- A. The node where the job is running is being drained for maintenance.
- B. Another job with a higher guaranteed quota and higher priority needs the resources.
- C. The job is exceeding its memory limits.
- D. The job is using an outdated CUDA driver version.
- E. A higher priority job is requesting resources and preemption is enabled for that queue.
Answer: A,B,E
Explanation:
Preemption in Run.ai with ACM is typically triggered by: Another job with a higher guaranteed quota and higher priority needing the resources (ACM prioritizes based on quota and priority). The node being drained (Kubernetes initiates preemption to safely evacuate pods before maintenance). A higher priority job needing resources and preemption is enabled. Exceeding memory limits usually results in an 00M error, not preemption. An outdated CUDA driver could cause errors, but not typically preemption. Note that OOM can occur on containers when the available memory is exhausted, which can cause them to be killed
NEW QUESTION # 57
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