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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q38-Q43):

NEW QUESTION # 38
Your AI team is using Kubernetes to orchestrate a cluster of NVIDIA GPUs for deep learning training jobs.
Occasionally, some high-priority jobs experience delays because lower-priority jobs are consuming GPU resources. Which of the following actions would most effectively ensure that high-priority jobs are allocated GPU resources first?

A. Increase the number of GPUs in the cluster
B. Configure Kubernetes pod priority and preemption
C. Manually assign GPUs to high-priority jobs
D. Use Kubernetes node affinity to bind jobs to specific nodes

Answer: B

Explanation:
Configuring Kubernetes pod priority and preemption (B) ensures high-priority jobs get GPU resources first.
Kubernetes supports priority classes, allowing high-priority pods to preempt (evict) lower-priority pods when resources are scarce. Integrated with NVIDIA GPU Operator, this dynamically reallocates GPUs, minimizing delays without manual intervention.
* More GPUs(A) increases capacity but doesn't prioritize allocation.
* Manual assignment(C) is unscalable and inefficient.
* Node affinity(D) binds jobs to nodes but doesn't address priority conflicts.
NVIDIA's Kubernetes integration supports this feature (B).

NEW QUESTION # 39
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. Prometheus with Node Exporter
C. Nagios Monitoring System
D. NVIDIA DCGM (Data Center GPU Manager)

Answer: D

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 # 40
Which industry has seen the most significant impact from AI-driven advancements, particularly in optimizing supply chain management and improving customer experience?

A. Healthcare
B. Real Estate
C. Education
D. Retail

Answer: D

Explanation:
Retail has experienced the most significant impact from AI-driven advancements, particularly in optimizing supply chain management and enhancing customer experience. NVIDIA's AI solutions, such as those deployed with NVIDIA DGX systems and Triton Inference Server, enable retailers to leverage deep learning for real-time inventory management, demand forecasting, and personalized recommendations. According to NVIDIA's "State of AI in Retail and CPG" survey report, AI adoption in retail has led to use cases like supply chain optimization (e.g., reducing stockouts) and customer experience improvements (e.g., AI-powered recommendation systems). These advancements are powered by GPU-accelerated analytics and inference, which process vast datasetsefficiently.
Healthcare (A) benefits from AI in diagnostics and drug discovery (e.g., NVIDIA Clara), but its primary focus is not supply chain or customer experience. Education (B) uses AI for personalized learning, but its scale and impact are less pronounced in these areas. Real Estate (D) leverages AI for property valuation and market analysis, but it lacks the extensive supply chain and customer-facing applications seen in retail. NVIDIA's official documentation, including "AI Solutions for Enterprises" and retail-specific use cases, highlights retail as a leader in AI-driven transformation for these specific domains.

NEW QUESTION # 41
During routine monitoring of your AI data center, you notice that several GPU nodes are consistently reporting high memory usage but low compute usage. What is the most likely cause of this situation?

A. The GPU drivers are outdated and need updating
B. The workloads are being run with models that are too small for the available GPUs
C. The power supply to the GPU nodes is insufficient
D. The data being processed includes large datasets that are stored in GPU memory but not efficiently utilized by the compute cores

Answer: D

Explanation:
The most likely cause is thatthe data being processed includes large datasets that are stored in GPU memory but not efficiently utilized by the compute cores(D). This scenario occurs when a workload loads substantial data into GPU memory (e.g., large tensors or datasets) but the computation phase doesn't fully leverage the GPU's parallel processing capabilities, resulting in high memory usage and low compute utilization. Here's a detailed breakdown:
* How it happens: In AI workloads, especially deep learning, data is often preloaded into GPU memory (e.g., via CUDA allocations) to minimize transfer latency. If the model or algorithm doesn't scale its compute operations to match the data size-due to small batch sizes, inefficient kernel launches, or suboptimal parallelization-the GPU cores remain underutilized while memory stays occupied. For example, a small neural network processing a massive dataset might only use a fraction of the GPU's thousands of cores, leaving compute idle.
* Evidence: High memory usage indicates data residency, while low compute usage (e.g., via nvidia-smi) shows that the CUDA cores or Tensor Cores aren't being fully engaged. This mismatch is common in poorly optimized workloads.
* Fix: Optimize the workload by increasing batch size, using mixed precision to engage Tensor Cores, or redesigning the algorithm to parallelize compute tasks better, ensuring data in memory is actively processed.
Why not the other options?
* A (Insufficient power supply): This would cause system instability or shutdowns, not a specific memory-compute imbalance. Power issues typically manifest as crashes, not low utilization.
* B (Outdated drivers): Outdated drivers might cause compatibility or performance issues, but they wouldn't selectively increase memory usage while reducing compute-symptoms would be more systemic (e.g., crashes or errors).
* C (Models too small): Small models might underuse compute, but they typically require less memory, not more, contradicting the high memory usage observed.
NVIDIA's optimization guides highlight efficient data utilization as key to balancing memory and compute (D).

NEW QUESTION # 42
Which of the following NVIDIA compute platforms is best suited for deploying AI workloads at the edge with minimal latency?

A. NVIDIA Jetson
B. NVIDIA Tesla
C. NVIDIA RTX
D. NVIDIA GRID

Answer: A

Explanation:
NVIDIA Jetson (D) is best suited for deploying AI workloads at the edge with minimal latency. The Jetson family (e.g., Jetson Nano, AGX Xavier) is designed for compact, power-efficient edge computing, delivering real-time AI inference for applications like IoT, robotics, and autonomous systems. It integrates GPU, CPU, and I/O in a single module, optimized for low-latency processing on-site.
* NVIDIA GRID(A) is for virtualized GPU sharing, not edge deployment.
* NVIDIA Tesla(B) is a data center GPU, too power-hungry for edge use.
* NVIDIA RTX(C) targets gaming/workstations, not edge-specific needs.
Jetson's edge focus is well-documented by NVIDIA (D).

NEW QUESTION # 43
......

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NVIDIA NCA-AIIO preparation labs - Pass4sure NCA-AIIO exam cram

The Best Accurate Latest NCA-AIIO Exam Dumps - Easy and Guaranteed NCA-AIIO Exam Success

NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q38-Q43):

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