NCA-AIIO LATEST TEST LABS, NCA-AIIO LATEST PRACTICE MATERIALS

NCA-AIIO Latest Test Labs, NCA-AIIO Latest Practice Materials

NCA-AIIO Latest Test Labs, NCA-AIIO Latest Practice Materials

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Tags: NCA-AIIO Latest Test Labs, NCA-AIIO Latest Practice Materials, NCA-AIIO Latest Exam Price, New NCA-AIIO Exam Pdf, NCA-AIIO Pass Guaranteed

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q195-Q200):

NEW QUESTION # 195
In managing an AI data center, you need to ensure continuous optimal performance and quickly respond to any potential issues. Which monitoring tool or approach would best suit the need to monitor GPU health, usage, and performance metrics across all deployed AI workloads?

  • A. Splunk
  • B. Nagios Monitoring System
  • C. NVIDIA DCGM (Data Center GPU Manager)
  • D. Prometheus with Node Exporter

Answer: C

Explanation:
NVIDIA DCGM (Data Center GPU Manager) is the best tool for monitoring GPU health, usage, and performance metrics across AI workloads in a data center. DCGM provides real-time insights into GPU- specific metrics (e.g., memory usage, utilization, power, errors), designed for NVIDIA GPUs in enterprise environments like DGX clusters. It integrates with orchestration tools (e.g., Kubernetes) and supports proactive issue detection, as detailed in NVIDIA's "DCGM User Guide." Nagios (A) and Prometheus (B) are general-purpose monitoring tools, lacking GPU-specific depth. Splunk (C) is a log analytics platform, not optimized for GPU monitoring. DCGM is NVIDIA's dedicated solution for AI data center management.


NEW QUESTION # 196
You are working on deploying a deep learning model that requires significant GPU resources across multiple nodes. You need to ensure that the model training is scalable, with efficient data transfer between the nodes to minimize latency. Which of the following networking technologies is most suitable for this scenario?

  • A. InfiniBand
  • B. Ethernet (1 Gbps)
  • C. Wi-Fi 6
  • D. Fiber Channel

Answer: A

Explanation:
InfiniBand (C) is the most suitable networking technology for scalable, low-latency data transfer in multi- node GPU training. It offers high throughput (up to 400 Gbps) and ultra-low latency (<1 µs), ideal for synchronizing gradients and weights across nodes using NVIDIA NCCL. InfiniBand's RDMA (Remote Direct Memory Access) further enhances efficiency by bypassing CPU overhead, critical for distributed deep learning.
* Wi-Fi 6(A) lacks the reliability and bandwidth (max ~10 Gbps) for training clusters.
* Fiber Channel(B) is for storage, not compute node interconnects.
* Ethernet (1 Gbps)(D) is too slow for large-scale AI training demands.
NVIDIA's DGX systems use InfiniBand for this purpose (C).


NEW QUESTION # 197
Which networking feature is most important for supporting distributed training of large AI models across multiple data centers?

  • A. High throughput with low latency WAN links between data centers
  • B. Segregated network segments to prevent data leakage between AI tasks
  • C. Implementation of Quality of Service (QoS) policies to prioritize AI training traffic
  • D. Deployment of wireless networking to enable flexible node placement

Answer: A

Explanation:
High throughput with low latency WAN links between data centers is the most important networking feature for supporting distributed training of large AI models. Distributed training across multiple data centers requires rapid exchange of gradients and model parameters, which demands high-bandwidth, low-latency connections (e.g., InfiniBand or high-speed Ethernet over WAN). NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure for Enterprise" emphasize that network performance is critical for scaling AI training geographically, ensuring synchronization and minimizing training time.
QoS policies (B) prioritize traffic but don't address raw performance needs. Segregated segments (C) enhance security, not training efficiency. Wireless networking (D) lacks the reliability and bandwidth for data center AI. NVIDIA prioritizes high-throughput, low-latency networking for distributed training.


NEW QUESTION # 198
You are managing a data center running numerous AI workloads on NVIDIA GPUs. Recently, some of the GPUs have been showing signs of underperformance, leading to slower job completion times. You suspect that resource utilization is not optimal. You need to implement monitoring strategies to ensure GPUs are effectively utilized and to diagnose any underperformance. Which of the following metrics is most critical to monitor for identifying underutilized GPUs in your data center?

  • A. Network Bandwidth Utilization
  • B. GPU Memory Usage
  • C. System Uptime
  • D. GPU Core Utilization

Answer: D

Explanation:
GPU Core Utilization is the most critical metric for identifying underutilized GPUs in an AI data center. This metric, accessible via NVIDIA's nvidia-smi or DCGM, measures the percentage of time GPU cores are actively processing tasks, directly indicating whether GPUs are underperforming due to idle time or poor workload distribution. Low core utilization suggests inefficient task scheduling or bottlenecks elsewhere (e.g., CPU, I/O). Option B (memory usage) is important but secondary, as high memory use doesn't guarantee core activity. Option C (network bandwidth) affects distributed workloads, not local GPU use. Option D (uptime) ensures availability, not utilization. NVIDIA's monitoring guidelines prioritize core utilization for performance diagnostics.


NEW QUESTION # 199
Your AI team is deploying a large-scale inference service that must process real-time data 24/7. Given the high availability requirements and the need to minimize energy consumption, which approach would best balance these objectives?

  • A. Use a single powerful GPU that operates continuously at full capacity to handle all inference tasks
  • B. Use a GPU cluster with a fixed number of GPUs always running at 50% capacity to save energy
  • C. Schedule inference tasks to run in batches during off-peak hours
  • D. Implement an auto-scaling group of GPUs that adjusts the number of active GPUs based on the workload

Answer: D

Explanation:
Implementing an auto-scaling group of GPUs (A) adjusts the number of active GPUs dynamically based on workload demand, balancing high availability and energy efficiency. This approach, supported by NVIDIA GPU Operator in Kubernetes or cloud platforms like AWS/GCP with NVIDIA GPUs, ensures 24/7 real-time processing by scaling up during peak loads and scalingdown during low demand, reducing idle power consumption. NVIDIA's power management features further optimize energy use per active GPU.
* Fixed GPU cluster at 50% capacity(B) wastes resources during low demand and may fail during peaks, compromising availability.
* Batch processing off-peak(C) sacrifices real-time capability, unfit for 24/7 requirements.
* Single GPU at full capacity(D) risks overload, lacks redundancy, and consumes maximum power continuously.
Auto-scaling aligns with NVIDIA's recommended practices for efficient, high-availability inference (A).


NEW QUESTION # 200
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