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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q74-Q79):

NEW QUESTION # 74
What technology allows the lowest networking latency for AI training in a data center?

Answer: C

Explanation:
InfiniBand provides ultra-low latency and high bandwidth communication, making it ideal for AI training in data centers where fast data transfer between nodes is critical for performance.


NEW QUESTION # 75
When setting up a virtualized environment with NVIDIA GPUs, you notice a significant drop in performance compared to running workloads on bare metal. Which factor is most likely contributing to the performance degradation?

Answer: A

Explanation:
Overcommitting GPU resources is the most likely cause of performance degradation in a virtualizedenvironment with NVIDIA GPUs. In virtualization setups using NVIDIA vGPU technology, overcommitting occurs when more virtual machines (VMs) request GPU resources than are physically available, leading to contention and reduced performance compared to bare metal. NVIDIA's vGPU documentation warns that proper resource allocation is critical to avoid this issue, as GPUs are not as easily time-sliced as CPUs. Option A (high-performance networking) typically enhances, not degrades, performance. Option C (SSD storage) improves I/O but doesn't directly impact GPU performance. Option D (high availability) adds redundancy, not significant GPU overhead. NVIDIA's guidelines emphasize avoiding overcommitment for optimal virtualized AI workloads.


NEW QUESTION # 76
Which NVIDIA tool aids data center monitoring and management?

Answer: B

Explanation:
NVIDIA Data Center GPU Manager (DCGM) aids data center monitoring and management by providing detailed GPU telemetry, health diagnostics, and performance tracking at scale. Clara targets healthcare, TensorRT optimizes inference, and Mellanox Insight isn't a standard NVIDIA tool, making DCGM the go-to solution.


NEW QUESTION # 77
When monitoring a GPU-based workload, what is GPU utilization?

Answer: B

Explanation:
GPU utilization is defined as the percentage of time the GPU's compute engines are actively processing data, reflecting its workload intensity over a period (e.g., via nvidia-smi). It's distinct from memory usage (a separate metric), core counts, or maximum runtime, providing a direct measure of compute activity.


NEW QUESTION # 78
A logistics company wants to optimize its delivery routes by predicting traffic conditions and delivery times.
The system must process real-time data from various sources, such as GPS, weather reports, and traffic sensors, to adjust routes dynamically. Which approach should the company use to effectively handle this complex scenario?

Answer: A

Explanation:
A deep learning model with a CNN to process multi-source real-time data (GPS, weather, traffic) is best for dynamic route optimization. CNNs excel at spatial data analysis, enabling accurate predictions on NVIDIA GPUs. Option A (decision trees) lacks real-time adaptability. Option B (unsupervised) doesn't predict dynamically. Option C (rule-based) is static. NVIDIA's logistics use cases endorse deep learning for real-time optimization.


NEW QUESTION # 79
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