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NVIDIA NCP-AIO Dumps Full Questions - Exam Study Guide

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NVIDIA AI Operations Sample Questions (Q40-Q45):

NEW QUESTION # 40
You need to upgrade the Run.ai platform on your on-premise Kubernetes cluster. What is the RECOMMENDED procedure for performing this upgrade to minimize downtime and ensure a smooth transition?

A. Follow the official Run.ai documentation for the specific upgrade path, typically involving a rolling update procedure using Helm or kubectl apply.
B. Edit the Run.ai deployment YAML file directly and apply the changes.
C. Delete the existing Run.ai deployment and install the new version from scratch.
D. Simply restart all Run.ai pods in the cluster.
E. Upgrade the Kubernetes cluster itself before upgrading Run.ai.

Answer: A

Explanation:
Following the official Run.ai documentation is crucial for a smooth upgrade. Run.ai typically provides detailed instructions for upgrading the platform, often involving a rolling update procedure using Helm or kubectl apply. This minimizes downtime by updating components incrementally. Deleting and reinstalling is disruptive and risky. Directly editing the deployment YAML is not recommended. While Kubernetes version compatibility is important, Run.ai upgrades should generally be performed independently, following Run.ai's documented procedure. Restarting pods is insufficient for a platform upgrade.

NEW QUESTION # 41
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.)

A. CPU
B. System Memory
C. PCIe
D. DPU
E. NIC

Answer: A,B

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 # 42
You have a cluster dedicated to AI inference, serving models from a persistent volume. You're experiencing high latency and CPU usage on the nodes serving inference requests. You suspect that storage access patterns are contributing to the issue. Your persistent volume is backed by a distributed file system. Describe a strategy, including relevant tools and techniques, to analyze the storage I/O profile of your inference workloads and identify potential optimizations.

A. Use 'iotop' or 'iostat' on the compute nodes to monitor real-time I/O activity and identify processes with high disk I/O. Then check the related containers that are doing more of these reads/writes.
B. Implement storage QOS (Quality of Service) policies to prioritize inference workloads and limit the impact of other I/O-intensive processes.
C. Capture network traffic using 'tcpdump' or Wireshark to analyze the communication patterns between the compute nodes and the storage system. Look for excessive network latency or congestion. Also monitor the network latency using tools like 'ping' or 'iperf.
D. Utilize the distributed file system's monitoring tools (if available) to analyze I/O patterns at the file system level. This can reveal hotspots or inefficient data access patterns.
E. Randomly restart the inference pods. If the issue goes away, it means the storage system was temporarily overloaded.

Answer: A,B,C,D

Explanation:
'iotopTiostat' identifies I/O-heavy processes. 'tcpdump'/Wireshark/ping/iperf helps analyze network communication. File system monitoring tools reveal data access patterns. Implementing storage QOS prioritizes inference workloads. Only restart the inference pods if you have a strong reason, otherwise troubleshooting the storage using one of the other methods is best practice.

NEW QUESTION # 43
Which command-line tool is primarily used for creating and managing MIG instances on NVIDIA GPUs?

A. nvprof
B. nvidia-smi
C. nvidia-cuda-smi
D. nvcc
E. nvidia-container-cli

Answer: B

Explanation:
'nvidia-smi' is the primary command-line tool for managing and monitoring NVIDIA GPUs, including creating and managing MIG instances. The other options have different purposes related to CUDA development and containerization.

NEW QUESTION # 44
A data scientist submits a Run.ai job requesting 4 GPUs. However, due to resource constraints, only 2 GPUs are immediately available. You want the job to automatically start running as soon as the remaining 2 GPUs become available, without manual intervention. How do you configure Run.ai to achieve this?

A. Configure a lower priority for the job.
B. Set the job's 'restartPolicy' to 'Always'.
C. Enable gang scheduling for the job.
D. Set a higher quota for the team.
E. Use Run.ai's 'suspend' and 'resume' commands manually.

Answer: C

Explanation:
Gang scheduling ensures that all requested resources (in this case, all 4 GPUs) are allocated before the job starts. The job will remain in a pending state until all resources are available, and then it will automatically start. 'restartPolicy only applies if a job fails after it has already started. Lower priority would make it less likely to start. Manually suspending and resuming requires intervention. A quota impacts how much you can submit overall, not the allocation of the complete resources requested by a single job.

NEW QUESTION # 45
......

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NVIDIA NCP-AIO Dumps Full Questions - Exam Study Guide

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NVIDIA AI Operations Sample Questions (Q40-Q45):

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