User-Friendly NVIDIA NCA-AIIO Exam Questions in PDF Format
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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q164-Q169):
NEW QUESTION # 164
Your AI training jobs are consistently taking longer than expected to complete on your GPU cluster, despite having optimized your model and code. Upon investigation, you notice that some GPUs are significantly underutilized. What could be the most likely cause of this issue?
Answer: C
Explanation:
An inefficient data pipeline causing bottlenecks is the most likely cause of prolonged training times and GPU underutilization in an optimized NVIDIA GPU cluster. If the data pipeline (e.g., I/O, preprocessing) cannot feed data to GPUs fast enough, GPUs idle, reducing utilization and extending training duration. NVIDIA's
"AI Infrastructure and Operations Fundamentals" and "Deep Learning Institute (DLI)" stress that data pipeline efficiency is a common bottleneck in GPU-accelerated training, detectable via tools like NVIDIA DCGM.
Insufficient power (A) would cause crashes, not underutilization. Inadequate cooling (C) leads to throttling, typically with high utilization. Outdated drivers (D) might degrade performance uniformly, not selectively.
NVIDIA's diagnostics point to data pipelines as the primary culprit here.
NEW QUESTION # 165
You are part of a team that is setting up an AI infrastructure using NVIDIA's DGX systems. The infrastructure is intended to support multiple AI workloads, including training, inference, and dataanalysis.
You have been tasked with analyzing system logs to identify performance bottlenecks under the supervision of a senior engineer. Which log file would be most useful to analyze when diagnosing GPU performance issues in this scenario?
Answer: D
Explanation:
NVIDIA GPU utilization logs from nvidia-smi are most useful for diagnosing GPU performance issues on DGX systems. These logs provide real-time metrics (e.g., utilization, memory usage, processes), pinpointing bottlenecks like underutilization or contention. Option A (network logs) aids distributed issues, not GPU- specific ones. Option C (kernel logs) tracks system events, not GPU performance. Option D (application logs) focuses on software, not hardware. NVIDIA's DGX troubleshooting guides prioritize nvidia-smi for GPU diagnostics.
NEW QUESTION # 166
In an AI infrastructure setup, you need to optimize the network for high-performance data movement between storage systems and GPU compute nodes. Which protocol would be most effective for achieving low latency and high bandwidth in this environment?
Answer: B
Explanation:
Remote Direct Memory Access (RDMA) is the most effective protocol for optimizing network performance between storage systems and GPU compute nodes in an AI infrastructure. RDMA enables direct memory access between devices over high-speed interconnects (e.g., InfiniBand, RoCE), bypassing the CPU and reducing latency while providing high bandwidth. This is critical for AI workloads, where large datasets must move quickly to GPUs for training or inference, minimizing bottlenecks.
HTTP (A) and SMTP (B) are application-layer protocols for web and email, respectively, unsuitable for low- latency data movement. TCP/IP (D) is a general-purpose networking protocol but lacks the performance of RDMA for GPU-centric workloads. NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure and Operations" materials highlight RDMA's role in high-performance AI networking.
NEW QUESTION # 167
Which components are essential parts of the NVIDIA software stack in an AI environment? (Select two)
Answer: D,E
Explanation:
The NVIDIA software stack for AI environments includes:
* NVIDIA CUDA Toolkit(A), a foundational platform for GPU-accelerated computing, enabling developers to program GPUs for AI tasks like training and inference.
* NVIDIA TensorRT(B), a high-performance inference library that optimizes deep learning models for deployment on NVIDIA GPUs, critical for AI workloads.
* NVIDIA JetPack SDK(C) is for edge devices (e.g., Jetson), not a core AI data center component.
* NVIDIA Nsight Systems(D) is a profiling tool, useful but not essential to the runtime stack.
* NVIDIA GameWorks(E) is for gaming, unrelated to AI.
CUDA and TensorRT are pillars of NVIDIA's AI ecosystem (A and B).
NEW QUESTION # 168
You have deployed an AI training job on a GPU cluster, but the training time has not decreased as expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is low, and the CPU utilization is very high. What is the most likely cause of this issue?
Answer: D
Explanation:
The data preprocessing being bottlenecked by the CPU is the most likely cause. High CPU utilization and low GPU utilization suggest the GPUs are idle, waiting for data, a common issue when preprocessing (e.g., data loading) is CPU-bound. NVIDIA recommends GPU-accelerated preprocessing (e.g., DALI) to mitigate this.
Option A (model incompatibility) would show errors, not low utilization. Option B (connection issues) would disrupt communication, not CPU load. Option C (software version) is less likely without specific errors.
NVIDIA's performance guides highlight preprocessing bottlenecks.
NEW QUESTION # 169
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