Reading DumpsBase’s NCA-AIIO Free Dumps (Part 3, Q81-Q120): More Sample Questions Online for Checking the NVIDIA NCA-AIIO Dumps (V8.02)

1. In a complex AI-driven autonomous vehicle system, the computing infrastructure is composed of multiple GPUs, CPUs, and DPUs.

During real-time object detection, which of the following best explains how these components interact to optimize performance?

2. 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?

3. A financial services company is developing a machine learning model to detect fraudulent transactions in real-time. They need to manage the entire AI lifecycle, from data preprocessing to model deployment and monitoring.

Which combination of NVIDIA software components should they integrate to ensure an efficient and scalable AI development and deployment process?

4. In an effort to optimize your data center for AI workloads, you deploy NVIDIA DPUs to offload network and security tasks from CPUs. Despite this, your AI applications still experience high latency during peak processing times.

What is the most likely cause of the latency, and how can it be addressed?

5. Which of the following best describes how memory and storage requirements differ between training and inference in AI systems?

6. In your AI data center, you’ve observed that some GPUs are underutilized while others are frequently maxed out, leading to uneven performance across workloads.

Which monitoring tool or technique would be most effective in identifying and resolving these GPU utilization imbalances?

7. You are responsible for managing an AI infrastructure that runs a critical deep learning application. The application experiences intermittent performance drops, especially when processing large datasets. Upon investigation, you find that some of the GPUs are not being fully utilized while others are overloaded, causing the overall system to underperform.

What would be the most effective solution to address the uneven GPU utilization and optimize the performance of the deep learning application?

8. 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 you use to allocate GPU resources across the cluster?

9. Your AI team is working on a complex model that requires both training and inference on large datasets. You notice that the training process is extremely slow, even with powerful GPUs, due to frequent data transfer between the CPU and GPU.

Which approach would best minimize these data transfer bottlenecks and accelerate the training process?

10. A data science team compares two regression models for predicting housing prices. Model X has an R-squared value of 0.85, while Model Y has an R-squared value of 0.78. However, Model Y has a lower Mean Absolute Error (MAE) than Model X.

Based on these statistical performance metrics, which model should be chosen for deployment, and why?

11. You are part of a team investigating the performance variability of an AI model across different hardware configurations. The model is deployed on various servers with differing GPU types, memory sizes, and CPU clock speeds. Your task is to identify which hardware factors most significantly impact the model's inference time.

Which analysis approach would be most effective in identifying the hardware factors that significantly impact the model’s inference time?

12. A healthcare provider is deploying an AI-driven diagnostic system that analyzes medical images to detect diseases. The system must operate with high accuracy and speed to support doctors in real-time. During deployment, it was observed that the system's performance degrades when processing high-resolution images in real-time, leading to delays and occasional misdiagnoses.

What should be the primary focus to improve the system’s real-time processing capabilities?

13. Which of the following best describes a key difference between training and inference architectures in AI deployments?

14. You are working with a large healthcare dataset containing millions of patient records. Your goal is to identify patterns and extract actionable insights that could improve patient outcomes. The dataset is highly dimensional, with numerous variables, and requires significant processing power to analyze effectively.

Which two techniques are most suitable for extracting meaningful insights from this large,

complex dataset? (Select two)

15. Your organization runs multiple AI workloads on a shared NVIDIA GPU cluster. Some workloads are more critical than others. Recently, you've noticed that less critical workloads are consuming more GPU resources, affecting the performance of critical workloads.

What is the best approach to ensure that critical workloads have priority access to GPU resources?

16. You have completed a data mining project and have discovered several key insights from a large and complex dataset. You now need to present these insights to stakeholders in a way that clearly communicates the findings and supports data-driven decision-making.

Which of the following approaches would be most effective for visualizing insights from large datasets to support decision-making in AI projects? (Select two)

17. You are part of a team analyzing the results of a machine learning experiment that involved training models with different hyperparameter settings across various datasets. The goal is to identify trends in

how hyperparameters and dataset characteristics influence model performance, particularly accuracy and overfitting.

Which analysis method would best help in identifying the relationships between hyperparameters, dataset characteristics, and model performance?

18. Your AI team notices that the training jobs on your NVIDIA GPU cluster are taking longer than expected. Upon investigation, you suspect underutilization of the GPUs.

Which monitoring metric is the most critical to determine if the GPUs are being underutilized?

19. Your company is running a distributed AI application that involves real-time data ingestion from IoT devices spread across multiple locations. The AI model processing this data requires high throughput and low latency to deliver actionable insights in near real-time. Recently, the application has been experiencing intermittent delays and data loss, leading to decreased accuracy in the AI model's predictions.

Which action would BEST improve the performance and reliability of the AI application in this scenario?

20. You are planning to deploy a large-scale AI training job in the cloud using NVIDIA GPUs.

Which of the following factors is most crucial to optimize both cost and performance for your deployment?

21. During the evaluation phase of an AI model, you notice that the accuracy improves initially but plateaus and then gradually declines.

What are the two most likely reasons for this trend? (Select two)

22. You are optimizing an AI inference pipeline for a real-time video analytics application that processes video streams from multiple cameras using deep learning models. The pipeline is running on a GPU cluster, but you notice that some GPU resources are underutilized while others are overloaded, leading to inconsistent processing times.

Which strategy would best balance the load across the GPUs and ensure consistent performance?

23. You are tasked with creating a real-time dashboard for monitoring the performance of a large-scale AI system processing social media data. The dashboard should provide insights into trends, anomalies, and performance metrics using NVIDIA GPUs for data processing and visualization.

Which tool or technique would most effectively leverage the GPU resources to visualize real-time insights from this high-volume social media data?

24. Which NVIDIA solution is specifically designed for simulating complex, large-scale AI workloads in a multi-user environment, particularly for collaborative projects in industries like robotics, manufacturing, and entertainment?

25. Which statement correctly differentiates between AI, machine learning, and deep learning?

26. 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?

27. 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?

28. 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?

29. 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)

30. 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?

31. 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?

32. 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?

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

34. 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?

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

36. 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?

37. 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?

38. 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?

39. 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?

40. 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?