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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q103-Q108):

NEW QUESTION # 103
You manage a large-scale AI infrastructure where several AI workloads are executed concurrently across multiple NVIDIA GPUs. Recently, you observe that certain GPUs are underutilized while others are overburdened, leading to suboptimal performance and extended processing times. Which of the following strategies is most effective in resolving this imbalance?

A. Increasing the power limit on underutilized GPUs
B. Implementing dynamic GPU load balancing across the infrastructure
C. Reducing the batch size for all AI workloads
D. Disabling GPU overclocking to normalize performance

Answer: B

NEW QUESTION # 104
A company is deploying a large-scale AI training workload that requires distributed computing across multiple GPUs. They need to ensure efficient communication between GPUs on different nodes and optimize the training time. Which of the following NVIDIA technologies should they use to achieve this?

A. NVIDIA NCCL (NVIDIA Collective Communication Library)
B. NVIDIA NVLink
C. NVIDIA TensorRT
D. NVIDIA DeepStream SDK

Answer: A

Explanation:
NVIDIA NCCL (NVIDIA Collective Communication Library) is the optimal technology for ensuring efficient communication between GPUs across different nodes in a distributed AI training workload. NCCL is a library specifically designed for multi-GPU and multi-node communication, providing optimized collective operations (e.g., all-reduce, broadcast) that minimize latency and maximize bandwidth. It integrates with high- speed interconnects like NVLink (within a node) and InfiniBand (across nodes), making it ideal for large- scale training where GPUs must synchronize gradients and parameters efficiently to reduce training time.
NVIDIA NVLink (A) is a high-speed interconnect for GPU-to-GPU communication within a single node, but it does not address inter-node communication across a cluster. NVIDIA TensorRT (B) is an inference optimization library, not suited for training workloads. NVIDIA DeepStream SDK (D) focuses on real-time video processing and inference, not distributed training. Official NVIDIA documentation, such as the "NCCL Developer Guide" and "AI Infrastructure and Operations Fundamentals" course, confirms NCCL's role in optimizing distributed training performance.

NEW QUESTION # 105
What is the primary advantage of using virtualized environments for AI workloads in a large enterprise setting?

A. Reduces the need for specialized hardware by running AI workloads on general-purpose CPUs
B. Allows for easier scaling of AI workloads across multiple physical machines
C. Ensures that AI workloads are always running on the same physical machine for consistency
D. Enables AI workloads to utilize cloud resources without requiring any changes to the underlying code

Answer: B

Explanation:
Virtualized environments, such as those using NVIDIA vGPU or GPU passthrough, enable easier scaling of AI workloads across multiple physical machines by abstracting hardware resources. This allows enterprises to dynamically allocate GPUs to virtual machines (VMs) based on demand, supporting growth without physical reconfiguration. NVIDIA's virtualization solutions (e.g., GRID, vGPU Manager) integrate with platforms like VMware or Kubernetes, facilitating seamless scalingin data centers or hybrid clouds, a key advantage in enterprise AI deployments.
Option A is incorrect-AI workloads still require GPUs, not just CPUs. Option C contradicts virtualization's flexibility, as it doesn't tie workloads to one machine. Option D overstates compatibility; code may still need adjustments for cloud APIs. Scaling is the primary benefit, per NVIDIA's virtualization strategy.

NEW QUESTION # 106
You are working with a team of data scientists on an AI project where multiple machine learning models are being trained to predict customer churn. The models are evaluated based on the Mean Squared Error (MSE) as the loss function. However, one model consistently shows a higher MSE despite having a more complex architecture compared to simpler models. What is the most likely reason for the higher MSE in the more complex model?

A. Underfitting due to insufficient model complexity
B. Overfitting to the training data
C. Low learning rate in model training
D. Incorrect calculation of the loss function

Answer: B

Explanation:
A complex model with higher MSE than simpler ones likely suffers from overfitting, where it learns training data noise rather than general patterns, reducing test performance. NVIDIA's training workflows (e.g., DGX, RAPIDS) emphasize regularization (e.g., dropout) to mitigate this, common in deep learning.
A low learning rate (Option A) slows convergence but doesn't inherently raise MSE. Incorrect loss calculation (Option C) would affect all models. Underfitting (Option D) contradicts the model's complexity.
Overfitting is NVIDIA-aligned for such scenarios.

NEW QUESTION # 107
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?

A. TCP/IP over Ethernet
B. VLAN segmentation
C. Network Address Translation (NAT)
D. InfiniBand with RDMA (Remote Direct Memory Access)

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 # 108
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Free PDF Quiz Fantastic NCA-AIIO - NVIDIA-Certified Associate AI Infrastructure and Operations Practice Guide

Pass Guaranteed NVIDIA - NCA-AIIO - Updated NVIDIA-Certified Associate AI Infrastructure and Operations Practice Guide

NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q103-Q108):

Actual NVIDIA NCA-AIIO Exam Question For Quick Success

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