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

NEW QUESTION # 195
You are comparing several regression models that predict the future sales of a product based on historical data. The models vary in complexity and computational requirements. Your goal is to select the model that provides the best balance between accuracy and the ability to generalize to new data. Which performance metric should you prioritize to select the most reliable regression model?

A. R-squared (Coefficient of Determination)
B. Mean Squared Error (MSE)
C. Accuracy
D. Cross-Entropy Loss

Answer: A

Explanation:
R-squared (Coefficient of Determination) is the performance metric to prioritize when selecting a regression model that balances accuracy and generalization. R-squared measures the proportion of variance in the dependent variable (sales) explained by the independent variables, ranging from 0 to 1. A higher R-squared indicates better fit, but when paired with techniques like cross-validation, italso reflects the model's ability to generalize to new data, avoiding overfitting. This aligns with NVIDIA's AI development best practices, which emphasize robust model evaluation for real-world deployment.
Mean Squared Error (MSE) (A) quantifies prediction error but does not directly assess generalization.
Accuracy (B) is for classification, not regression. Cross-Entropy Loss (D) is for classification tasks, irrelevant here. NVIDIA's "Deep Learning Institute (DLI)" training and "AI Infrastructure and Operations" materials recommend R-squared for regression model selection.

NEW QUESTION # 196
Which of the following software components is most responsible for optimizing deep learning operations on NVIDIA GPUs by providing highly tuned implementations of standard routines?

A. TensorFlow
B. CUDA
C. cuDNN
D. NCCL

Answer: C

Explanation:
NVIDIA cuDNN (CUDA Deep Neural Network library) is specifically designed to optimize deep learning operations on NVIDIA GPUs by providing highly tuned implementations of standard routines, such as convolutions, pooling, and activation functions. It underpins frameworks like TensorFlow and PyTorch, accelerating training and inference in NVIDIA's ecosystem (e.g., DGX, Jetson). cuDNN's optimizations leverage GPU parallelism, making it the core component for deep learning performance.
CUDA (Option A) is a general-purpose GPU programming platform, not specialized for deep learning.
TensorFlow (Option B) is a framework that uses cuDNN, not the optimizer itself. NCCL (Option D) focuses on multi-GPU communication, not individual operations. cuDNN is NVIDIA's flagship deep learning optimization tool.

NEW QUESTION # 197
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?

A. Using high-performance networking.
B. Enabling high availability features.
C. Running VMs on SSD storage.
D. Overcommitting GPU resources.

Answer: D

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 # 198
You are responsible for managing an AI data center that handles large-scale deep learning workloads. The performance of your training jobs has recently degraded, and you've noticed that the GPUs are underutilized while CPU usage remains high. Which of the following actions would most likely resolve this issue?

A. Add more GPUs to the system.
B. Reduce the batch size during training.
C. Optimize the data pipeline for better I/O throughput.
D. Increase the GPU memory allocation.

Answer: C

Explanation:
GPU underutilization with high CPU usage during training suggests a bottleneck in the data pipeline, where CPUs can't feed data to GPUs fast enough, starving them of work. Optimizing the data pipeline for better I/O throughput-using NVIDIA DALI for GPU-accelerated data loading or improving storage (e.g., NVMe SSDs)
-ensures data reaches GPUs efficiently, maximizing utilization. This is a common issue in NVIDIA DGX systems, where pipeline optimization is critical for large-scale workloads.
Increasing GPU memory (Option A) doesn't address data delivery. Reducing batch size (Option B) might lower GPU demand but reduces throughput, not solving the root cause. Adding GPUs (Option C) exacerbates underutilization without fixing the bottleneck. NVIDIA's training optimization guides prioritize pipeline efficiency.

NEW QUESTION # 199
You are managing a high-performance AI cluster where multiple deep learning jobs are scheduled to run concurrently. To maximize resource efficiency, which of the following strategies should youuse to allocate GPU resources across the cluster?

A. Use a priority queue to assign GPUs to jobs based on their deadline, ensuring the most time-sensitive jobs complete first.
B. Allocate all GPUs to the largest job to ensure its rapid completion, then proceed with smaller jobs.
C. Allocate GPUs to jobs based on their compute intensity, reserving the most powerful GPUs for the most demanding tasks.
D. Assign jobs to GPUs based on their geographic proximity to reduce data transfer times.

Answer: C

Explanation:
Maximizing resource efficiency in a high-performance AI cluster requires matching GPU capabilities to job requirements. Allocating GPUs based on compute intensity ensures that resource-intensive tasks (e.g., large models or datasets) run on high-performance GPUs (e.g., NVIDIA A100 or H100), while lighter tasks use less powerful ones (e.g., V100). NVIDIA's Multi-Instance GPU (MIG) and GPU Operator in Kubernetes support this strategy by allowing dynamic partitioning and allocation, optimizing utilization and throughput across the cluster.
A priority queue (Option A) focuses on deadlines but may underutilize GPUs if low-priority jobs are resource- heavy. Allocating all GPUs to one job (Option B) wastes resources when smaller jobs could run concurrently.
Geographic proximity (Option D) reduces latency in distributed setups but doesn't address compute efficiency within a cluster. NVIDIA's emphasis on workload-aware scheduling in DGX and cloud environments supports Option C as the best approach.

NEW QUESTION # 200
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