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NVIDIA AI Operations Sample Questions (Q18-Q23):
NEW QUESTION # 18
You are using NVIDIA MPS (Multi-Process Service) to allow multiple CUDA applications to share a single GPU. One of the applications is consistently crashing. How can you isolate the faulty application using MPS?
Answer: A,C
Explanation:
The most direct approach is to disable MPS and run each application independently to pinpoint the source of the crashes. Examining the system logs for error messages linked to specific PIDs helps identify the failing process. Monitoring GPU utilization (B) might provide hints, but it doesn't directly isolate the faulty application. Reducing threads (D) might mask the issue, but it doesn't solve it. Restarting the server (E) is a temporary solution and doesn't address the root cause.
NEW QUESTION # 19
A system administrator needs to lower latency for an AI application by utilizing GPUDirect Storage.
What two (2) bottlenecks are avoided with this approach? (Choose two.)
Answer: D,E
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
GPUDirect Storage allows data to be transferred directly from storage to GPU memory,bypassing the CPU and system memory. This reduces latency and overhead by avoiding data movement through the CPU and main memory, accelerating data feeding to GPUs for AI workloads. PCIe and NIC are still involved in the data path, and the DPU may participate depending on architecture but are not the primary bottlenecks avoided by GPUDirect Storage.
NEW QUESTION # 20
You've deployed a container from NGC containing a computationally intensive AI model training script. You notice that the container is consistently being killed by the Kubernetes OOMKiller, even though the node has sufficient memory available. What are the possible causes and solutions?
Answer: A,C,D,E
Explanation:
An insufficient memory limit triggers the OOMKiller. Memory leaks cause excessive consumption. Increasing the limit and fixing leaks are solutions. C, while a potential issue in some environments, is less likely than the container-specific reasons in a Kubernetes environment.
NEW QUESTION # 21
You are using BeeGFS as a shared file system for your AI training cluster. You observe that some nodes are experiencing significantly lower read performance compared to others. How would you approach troubleshooting this performance discrepancy, considering the BeeGFS architecture?
Answer: A,B,C,E
Explanation:
Verifying client version consistency ensures compatibility. Network connectivity is crucial for communication with BeeGFS servers. Client logs provide error information. Data locality ensures data resides closer to the compute nodes. Restarting the whole cluster is not the right choice, and you should investigate the root cause first.
NEW QUESTION # 22
You are setting up a multi-tenant Run.ai cluster. Two teams, 'Team Alpha' and 'Team Beta', require access. You want to ensure 'Team Alpha' always has priority access to GPUs and cannot be starved of resources, even when 'Team Beta' submits a large number of jobs.
Which Run.ai configuration option BEST achieves this?
Answer: A,B
Explanation:
Configuring a higher priority within the fair-share scheduler ensures 'Team Alpha' gets preferential access to resources. Additionally, implementing preemption allows 'Team Alpha' to reclaim resources from 'Team Beta' if needed. While node affinity could provide dedicated resources, it doesn't dynamically address resource contention when 'Team Alpha' needs more than its dedicated nodes. Equal quotas and disabling the scheduler do not provide priority. Note that in new run.ai setups, ACM will be configured and you configure fair-share at ACM.
NEW QUESTION # 23
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