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NEW QUESTION # 123
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?
Answer: B
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 # 124
In an AI infrastructure setup using NVIDIA GPUs across multiple nodes, you notice that the inter-node communication latency is higher than expected during distributed training. Which networking feature or protocol is most likely responsible for reducing latency in this scenario?
Answer: D
Explanation:
InfiniBand with RDMA (Remote Direct Memory Access) is the most effective networking feature for reducing inter-node communication latency in distributed training on NVIDIA GPUs. InfiniBand, paired with RDMA, enables direct memory access between nodes, bypassing CPU overhead and achieving ultra-low latency and high bandwidth (e.g., 200 Gb/s), critical for GPU-to-GPU data transfers via NVLink or NCCL.
Option A (NAT) manages addressing, not latency. Option B (TCP/IP over Ethernet) has higher overhead than InfiniBand. Option D (VLAN segmentation) aids isolation, not speed. NVIDIA's DGX and cluster documentation recommend InfiniBand for distributed AI workloads.
NEW QUESTION # 125
What is an advantage of on-premise GPU resources?
Answer: C
Explanation:
On-premise GPU resources provide data sovereignty, ensuring that sensitive data remains within the organization's control and complies with local regulations, unlike public cloud solutions.
NEW QUESTION # 126
A large manufacturing company is implementing an AI-based predictive maintenance system to reduce downtime and increase the efficiency of its production lines. The AI system must analyze data from thousands of sensors in real-time to predict equipment failures before they occur. However, during initial testing, the system fails to process the incoming data quickly enough, leading to delayed predictions and occasional missed failures. What would be the most effective strategy to enhance the system's real-time processing capabilities?
Answer: B
Explanation:
Implementing edge computing to preprocess sensor data closer to the source is the most effective strategy to enhance real-time processing capabilities for a predictive maintenance system. Using NVIDIA Jetson devices at the edge, raw sensor data can be filtered, aggregated, or preprocessed (e.g., via DeepStream), reducing the volume sent to the central GPU cluster (e.g., DGX). This lowers latency and ensures timely predictions, as outlined in NVIDIA's "Edge AI Solutions" and "AI Infrastructure for Enterprise." Reducing sensors (A) risks missing critical data. A more complex model (B) increases processingdemands, worsening delays. Higher data frequency (D) exacerbates the bottleneck. Edge computing is NVIDIA's recommended solution for real-time IoT workloads.
NEW QUESTION # 127
A healthcare provider is deploying an AI-driven diagnostic system that analyzes medical images to detect diseases. The system must operate with high accuracy and speed to support doctors in real-time. During deployment, it was observed that the system's performance degrades when processing high-resolution images in real-time, leading to delays and occasional misdiagnoses. What should be the primary focus to improve the system's real-time processing capabilities?
Answer: B
Explanation:
Real-time medical image analysis demands high accuracy and speed, which degrade with high-resolution images due to computational complexity. Optimizing the AI model's architecture for better parallel processing on GPUs-using techniques like pruning, quantization, or TensorRT optimization-reduces latency while maintaining accuracy. NVIDIA GPUs (e.g., A100) and TensorRT are designed to accelerate such workloads, making this the primary focus for improvement in DGX or healthcare-focused deployments.
More memory (Option A) helps with batching but doesn't address processing speed. Switching to CPUs (Option C) slows performance, as they lack GPU parallelism. Lowering resolution (Option D) risks accuracy loss, undermining diagnostics. Model optimization aligns with NVIDIA's real-time AI strategy.
NEW QUESTION # 128
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