NCA-AIIO 시험 - NVIDIA실제시험문제와 답 - 300문항

Question No : 1

Your AI infrastructure team is deploying a large NLP model on a Kubernetes cluster using NVIDIA GPUs. The model inference requires low latency due to real-time user interaction. However, the team notices occasional latency spikes.
What would be the most effective strategy to mitigate these latency spikes?

A.Deploy the Model on Multi-Instance GPU (MIG) Architecture
B.Use NVIDIA Triton Inference Server with Dynamic Batching
C.Increase the Number of Replicas in the Kubernetes Cluster
D.Reduce the Model Size by Quantization

정답:

Question No : 2

In a large-scale AI cluster, you are responsible for managing job scheduling to optimize resource utilization and reduce job queuing times.
Which of the following job scheduling strategies would best achieve this goal?

A.Use a first-come, first-served (FCFS) scheduling policy to ensure fairness in job execution order.
B.Schedule jobs based on their estimated runtime, assigning longer jobs to the fastest GPUs to minimize overall completion time.
C.Assign jobs based on GPU idle time, ensuring that all GPUs are utilized as soon as they become available.
D.Implement preemptive scheduling to allow high-priority jobs to interrupt lower-priority ones, ensuring critical tasks are completed first.

정답:

Question No : 3

You are managing an AI infrastructure where multiple teams share GPU resources for different AI projects, including training deep learning models, running inference tasks, and conducting hyperparameter tuning. You notice that the GPU utilization is uneven, with some GPUs underutilized while others are overburdened.
What is the best approach to optimize GPU utilization across all teams?

A.Implement dynamic GPU resource allocation based on real-time workload demands
B.Prioritize deep learning training tasks over inference tasks
C.Allocate fixed GPU resources to each team based on their initial requirements
D.Limit the number of active tasks per team to avoid overloading GPUs

정답:

Question No : 4

Your organization is planning to deploy an AI solution that involves large-scale data processing, training, and real-time inference in a cloud environment. The solution must ensure seamless integration of data pipelines, model training, and deployment.
Which combination of NVIDIA software components will best support the entire lifecycle of this AI solution?

A.NVIDIA TensorRT + NVIDIA DeepStream SDK
B.NVIDIA RAPIDS + NVIDIA TensorRT
C.NVIDIA Triton Inference Server + NVIDIA NGC Catalog
D.NVIDIA RAPIDS + NVIDIA Triton Inference Server + NVIDIA NGC Catalog

정답:

Question No : 5

You are managing an AI infrastructure that includes multiple NVIDIA GPUs across various virtual machines (VMs) in a cloud environment. One of the VMs is consistently underperforming compared to others, even though it has the same GPU allocation and is running similar workloads.
What is the most likely cause of the underperformance in this virtual machine?

A.Inadequate storage I/O performance
B.Insufficient CPU allocation for the VM
C.Misconfigured GPU passthrough settings
D.Incorrect GPU driver version installed

정답:

Question No : 6

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

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

정답:

Question No : 7

Your team is tasked with deploying a new AI-driven application that needs to perform real-time video processing and analytics on high-resolution video streams. The application must analyze multiple video feeds simultaneously to detect and classify objects with minimal latency.
Considering the processing demands, which hardware architecture would be the most suitable for this scenario?

A.Use CPUs for video analytics and GPUs for managing network traffic.
B.Deploy a combination of CPUs and FPGAs for video processing.
C.Deploy GPUs to handle the video processing and analytics.
D.Deploy CPUs exclusively for all video processing tasks.

정답:

Question No : 8

Which of the following is a key design principle when constructing a data center specifically for AI workloads?

A.Maximizing the number of virtual machines (VMs) to increase resource utilization.
B.Ensuring GPU clusters are tightly integrated with high-bandwidth memory (HBM).
C.Focusing on traditional CPU overclocking to maximize compute performance.
D.Designing for minimal power consumption to reduce operational costs.

정답:

Question No : 9

Your AI model training process suddenly slows down, and upon inspection, you notice that some of the GPUs in your multi-GPU setup are operating at full capacity while others are barely being used.
What is the most likely cause of this imbalance?

A.Data loading process is not evenly distributed across GPUs.
B.GPUs are not properly installed in the server chassis.
C.Different GPU models are used in the same setup.
D.The AI model code is optimized only for specific GPUs.

정답:

Question No : 10

You are working on a high-performance AI workload that requires the deployment of deep learning models on a multi-GPU cluster. The workload needs to scale across multiple nodes efficiently while maintaining high throughput and low latency. However, during the deployment, you notice that the GPU utilization is uneven across the nodes, leading to performance bottlenecks.
Which of the following strategies would be the most effective in addressing the uneven GPU utilization in this multi-node AI deployment?

A.Use a CPU-based load balancer to distribute tasks.
B.Enable GPU affinity in the job scheduler.
C.Enable mixed precision training.
D.Increase the batch size of the workload.

정답:

Question No : 11

During a high-intensity AI training session on your NVIDIA GPU cluster, you notice a sudden drop in performance.
Suspecting thermal throttling, which GPU monitoring metric should you prioritize to confirm this issue?

A.GPU Clock Speed
B.Memory Bandwidth Utilization
C.GPU Temperature and Thermal Status
D.CPU Utilization

정답:

Question No : 12

Your team is tasked with analyzing a large dataset to extract meaningful insights that can be used to improve the performance of your AI models. The dataset contains millions of records from various sources, and you need to apply data mining techniques to uncover patterns and trends.
Which of the following data mining techniques would be most effective for discovering patterns in large datasets used in AI workloads? (Select two)

A.Overfitting the model to ensure it captures all possible patterns.
B.Using a flat file to store the entire dataset.
C.K-means clustering to group similar data points.
D.Principal Component Analysis (PCA) to reduce the dimensionality of the dataset.
E.Applying dropout to prevent the model from memorizing patterns.

정답:

Question No : 13

You are tasked with comparing two deep learning models, Model Alpha and Model Beta, both trained to recognize images of animals. Model Alpha has a Cross-Entropy Loss of 0.35, while Model Beta has a Cross-Entropy Loss of 0.50.
Which model should be considered better based on the Cross-Entropy Loss, and why?

A.Model Alpha is worse because a lower Cross-Entropy Loss suggests the model is underfitting.
B.Model Alpha is better because it has a lower Cross-Entropy Loss.
C.Model Beta is better because Cross-Entropy Loss measures model complexity, and higher is better.
D.Model Beta is better because it has a higher Cross-Entropy Loss.

정답:

Question No : 14

You are working on a project that involves monitoring the performance of an AI model deployed in production. The model's accuracy and latency metrics are being tracked over time. Your task, under the guidance of a senior engineer, is to create visualizations that help the team understand trends in these metrics and identify any potential issues.
Which visualization would be most effective for showing trends in both accuracy and latency metrics over time?

A.Pie chart showing the distribution of accuracy metrics.
B.Stacked area chart showing cumulative accuracy and latency.
C.Dual-axis line chart with accuracy on one axis and latency on the other.
D.Box plot comparing accuracy and latency.

정답:

Question No : 15

Your AI cluster handles a mix of training and inference workloads, each with different GPU resource requirements and runtime priorities.
What scheduling strategy would best optimize the allocation of GPU
resources in this mixed-workload environment?

A.Increase the GPU Memory Allocation for All Jobs
B.Use Kubernetes Node Affinity with Taints and Tolerations
C.Manually Assign GPUs to Jobs Based on Priority
D.Implement FIFO Scheduling Across All Jobs

정답:

NVIDIA NCA-AIIO 시험