NCP-AIO RELIABLE TORRENT & RELIABLE NCP-AIO EXAM CAMP

NCP-AIO Reliable Torrent & Reliable NCP-AIO Exam Camp

NCP-AIO Reliable Torrent & Reliable NCP-AIO Exam Camp

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Tags: NCP-AIO Reliable Torrent, Reliable NCP-AIO Exam Camp, Certified NCP-AIO Questions, Valid NCP-AIO Exam Labs, NCP-AIO Certification Torrent

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NVIDIA AI Operations Sample Questions (Q209-Q214):

NEW QUESTION # 209
You're using BCM to manage a cluster and need to upgrade the Kubernetes version. What considerations are critical to ensure a smooth upgrade process?

  • A. Ensure all worker nodes have sufficient resources (CPU, memory) for the new Kubernetes version.
  • B. Test the upgrade in a staging environment before applying it to the production cluster.
  • C. Back up the etcd database before starting the upgrade.
  • D. Drain nodes before upgrading them to minimize application downtime.
  • E. Update the NVIDIA drivers and container runtime on all nodes after the Kubernetes upgrade.

Answer: A,B,C,D

Explanation:
Backing up etcd is crucial for rollback. Resource sufficiency prevents upgrade failures. Testing in staging identifies potential issues. Draining minimizes downtime. NVIDIA drivers should ideally be checked for compatibility and potentially updated before the Kubernetes upgrade.


NEW QUESTION # 210
You are designing a data center network to support distributed deep learning training across multiple servers. The training job uses NCCL (NVIDIA Collective Communications Library) for inter-GPU communication. Which of the following network configurations will maximize the performance of NCCL?

  • A. A traditional three-tier network architecture with oversubscribed links at each layer.
  • B. A VLAN-based network with no QOS (Quality of Service) configured.
  • C. A Clos network topology with non-blocking links between all servers, utilizing RoCEv2 or InfiniBand.
  • D. A single network switch connecting all servers, with each server connected via a single IOGbE link.
  • E. A network using only TCP/IP without RDMA support.

Answer: C

Explanation:
NCCL benefits greatly from low-latency, high-bandwidth communication. A Clos network with non-blocking links, RoCEv2, or InfiniBand ensures that GPUs can communicate efficiently without bottlenecks. A single switch with limited bandwidth, a three-tier network with oversubscription, or lack of RDMA will significantly hinder NCCL performance. VLANs without QOS do not guarantee low latency.


NEW QUESTION # 211
A Kubernetes pod running a GPU-accelerated application is failing to start. The pod's logs show the error message 'Failed to initialize NVML: Driver/library version mismatch'. What are the MOST likely causes of this issue?

  • A. The 'nvidia-container-runtime' is not properly configured on the Kubernetes nodes.
  • B. The Kubernetes version is incompatible with the NVIDIA drivers.
  • C. The Docker daemon is not running.
  • D. The GPU is not properly seated in the server.
  • E. The NVIDIA driver version on the host is incompatible with the CUDA version used in the container image.

Answer: A,E

Explanation:
A 'Driver/library version mismatch' error strongly suggests an incompatibility between the host's NVIDIA driver and the CUDA libraries in the container. Improper configuration of the 'nvidia-container-runtime' (which handles GPU access for containers) is also a likely cause. While a loose GPU (C) would prevent GPU access altogether, it's less likely to cause this specific error. The Docker daemon (D) is essential for containers, but this specific error points to NVIDIA-related issues. Kubernetes version incompatibility (E) is possible, but less common than driver/CUDA mismatch.


NEW QUESTION # 212
You have multiple users sharing a server with a single NVIDIAA100 GPU. Two users, Alice and Bob, want to run deep learning experiments concurrently. Alice's job requires 20GB of GPU memory and 30% of compute, while Bob's job needs IOGB of GPU memory and 20% of compute. How can you use MIG to optimally configure the GPU to accommodate both users' requirements?

  • A. Create two MIG instances: one 3g.20gb instance for Alice and one lg.5gb instance for Bob.
  • B. Do not use MIG; let both users share the entire GPU.
  • C. Create one MIG instance for Alice and let Bob use the remaining GPU resources.
  • D. Create two MIG instances: one 4g.20gb instance for Alice and one 2g.10gb instance for Bob.
  • E. Create two MIG instances: one lg.5gb instance for Alice and one lg.5gb instance for Bob.

Answer: D

Explanation:
This question challenges understanding of MIG instance sizes. Options A and B are not correct because they allocate insufficient memory to Alice. Option C is not correct because it does not provide dedicated resources for Bob. Option E means that Alice's job is resource intensive. The correct answer is D because it ensures that both Alice and Bob get at least the memory they need and some compute resource allocation. 4g.20gb and 2g.10gb instances ensure allocation of resources required for both users independently.


NEW QUESTION # 213
You have a DOCA application deployed on a BlueField-3 DPU. The application utilizes multiple DOCA services, including DOCA Flow and DOCA DPI. You are experiencing performance issues, and you suspect that the bottleneck is within the DPU. How would you proceed with debugging and profiling the DOCA application to identify the source of the performance bottleneck?

  • A. Using perf: Utilize the 'perf tool on the DPU to profile the DOCA application and identify the functions or code sections that are consuming the most CPU cycles.
  • B. Using DOCA Telemetry: Enable DOCA Telemetry to collect performance metrics for different DOCA services and identify the service with the highest latency or resource consumption.
  • C. Using DOCA Tracing: Implement DOCA Tracing within the application code to record detailed information about function calls, memory allocations, and other events, then analyze the trace data to identify performance issues.
  • D. Analyzing DOCA logs: By analyzing DOCA Logs with debug mode, you can troubleshoot and profiling the DOCA application to identify the source of the performance bottleneck.
  • E. Using gdb: Attach 'gdb' to the DOCA application process and set breakpoints at strategic locations to analyze the application's behavior and identify performance bottlenecks.

Answer: A,B,C,E

Explanation:
DOCA Telemetry, 'perf, 'gdb' , and DOCA Tracing are all valuable tools for debugging and profiling DOCA applications. DOCA Telemetry provides high-level performance metrics, while 'perf and 'gdb' offer more detailed profiling and debugging capabilities. DOCA Tracing allows for recording and analyzing specific events within the application. Analyzing DOCA Logs for profiling is not a solution for debugging performance, as it only collects information about debug trace.


NEW QUESTION # 214
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