Free Access to NVIDIA.NCA-AIIO.v2025-09-29.q49 with Valid Practice Test (Page 9)

Question 36

Your team is tasked with deploying a deep learning model that was trained on large datasets for natural language processing (NLP). The model will be used in a customer support chatbot, requiring fast, real-time responses. Which architectural considerations are most important when moving from the training environment to the inference environment?

A.Data augmentation and hyperparameter tuning
B.Model checkpointing and distributed inference
C.Low-latency deployment and scaling
D.High memory bandwidth and distributed training

Question 37

You are tasked with contributing to the operations of an AI data center that requires high availability and minimal downtime. Which strategy would most effectively help maintain continuous AI operations in collaboration with the data center administrator?

A.Implement a failover system where DPUs manage the AI model inference during GPU downtime
B.Deploy a redundant set of CPUs to take over GPU workloads in case of failure
C.Use GPUs in active-passive clusters, with DPUs handling real-time network failover and security
D.Schedule regular maintenance during peak hours to ensure that GPUs and DPUs are always operational

Correct Answer: C

UsingGPUs in active-passive clusters, with DPUs handling real-time network failover and security(C) is the most effective strategy for maintaining continuous AI operations with high availability and minimal downtime. Let's explore this in depth:
* Active-Passive GPU Clusters: In this setup, active GPUs handle the primary workload (e.g., training or inference), while passive GPUs remain on standby, ready to take over if an active node fails. This redundancy ensures that AI operations continue seamlessly during hardware failures, a common high- availability design in data centers. NVIDIA's GPU clusters (e.g., DGX systems) support such configurations, often managed via orchestration tools like Kubernetes with the NVIDIA GPU Operator.
* Role of DPUs: NVIDIA's Data Processing Units (e.g., BlueField DPUs) offload network, storage, and security tasks from CPUs and GPUs, enhancing system resilience. In this strategy, DPUs manage real- time network failover (e.g., rerouting traffic to passive GPUs) and security (e.g., encryption, isolation), ensuring uninterrupted data flow and protection during failover events. This reduces latency and downtime compared to CPU-managed failover.
* Why it works: The combination leverages GPU redundancy for compute continuity and DPU intelligence for network reliability, aligning with NVIDIA's vision of integrated AI infrastructure.
Monitoring tools (e.g., nvidia-smi, DPU metrics) enable proactive failover triggers, minimizing disruption.
Why not the other options?
* A (DPU-managed inference during GPU downtime): DPUs accelerate networking/storage, not inference, which requires GPU compute power-making this impractical.
* B (CPU redundancy): CPUs can't match GPU performance for AI workloads, leading to degraded operation, not continuity.
* D (Peak-hour maintenance): Scheduling maintenance during peak hours increases downtime, contradicting the goal.
NVIDIA's DPU and GPU cluster documentation supports this high-availability approach (C).

Question 38

What is an advantage of InfiniBand over Ethernet?

A.InfiniBand offers lower latency than Ethernet.
B.InfiniBand supports RDMA while Ethernet does not.
C.InfiniBand always provides higher bandwidth than Ethernet.

Question 39

You are working on a project that involves analyzing a large dataset of satellite images to detect deforestation.
The dataset is too large to be processed on a single machine, so you need to distribute the workload across multiple GPU nodes in a high-performance computing cluster. The goal is to use image segmentation techniques to accurately identify deforested areas. Which approach would be most effective in processing this large dataset of satellite images for deforestation detection?

A.Implementing a distributed GPU-accelerated Convolutional Neural Network (CNN) for image segmentation
B.Storing the images in a traditional relational database for easy access and querying
C.Using a CPU-based image processing library to preprocess the images before segmentation
D.Manually reviewing the images and marking deforested areas for analysis

Question 40

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?

A.Network traffic logs
B.NVIDIA GPU utilization logs (nvidia-smi)
C.System kernel logs (dmesg)
D.Application error logs

Question 36

Question 37

Question 38

Question 39

Question 40

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