NVD-001 시험 - NVIDIA실제시험문제와 답 - 300문항

Question No : 1

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

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Question No : 2

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.

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Question No : 3

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.

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Question No : 4

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.

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Question No : 5

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

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Question No : 6

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

A.Machine learning is a type of AI that only uses linear models, while deep learning involves non-linear models
B.Machine learning is the same as AI, and deep learning is simply a method within AI that doesn't involve machine learning
C.AI is a broad field encompassing various technologies, including machine learning, which focuses on learning from data, while deep learning is a specialized type of machine learning that uses neural networks
D.Deep learning is a broader concept than machine learning, which is a specialized form of AI

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Question No : 7

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?

A.NVIDIA JetPack
B.NVIDIA TensorRT
C.NVIDIA Triton Inference Server
D.NVIDIA Omniverse

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Question No : 8

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?

A.Employing a GPU-accelerated time-series database for real-time data ingestion and visualization.
B.Using a standard CPU-based ETL (Extract, Transform, Load) process to prepare the data for visualization.
C.Relying solely on a relational database to handle the data and generate visualizations.
D.Implementing a GPU-accelerated deep learning model to generate insights and feeding results directly into the dashboard.

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Question No : 9

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?

A.Implement dynamic load balancing that assigns workloads to GPUs based on their current utilization and processing capacity.
B.Use a single GPU for each camera feed, regardless of the computational load.
C.Randomly distribute video streams across all available GPUs to maximize usage.
D.Allocate the most computationally intensive tasks to the GPU with the least memory usage.

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Question No : 10

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)

A.Learning rate too high, causing instability
B.Regularization techniques applied correctly
C.Inadequate dataset size for training
D.Using cross-validation for model evaluation
E.Overfitting of the model to the training data

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Question No : 11

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?

A.Using reserved instances instead of on-demand instances
B.Selecting instances with the highest available GPU core count
C.Ensuring data locality by choosing cloud regions closest to your data sources
D.Enabling autoscaling to dynamically allocate resources based on workload demand

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Question No : 12

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?

A.Implementing a dedicated, high-bandwidth network link between IoT devices and the data processing centers.
B.Switching to a batch processing model to reduce the frequency of data transfers.
C.Upgrading the IoT devices to more powerful hardware.
D.Deploying a Content Delivery Network (CDN) to cache data closer to the IoT devices.

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Question No : 13

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?

A.Memory Bandwidth Utilization
B.GPU Utilization Percentage
C.CPU Utilization
D.Network Latency

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Question No : 14

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?

A.Conduct a correlation matrix analysis between hyperparameters, dataset characteristics, and performance metrics.
B.Use a pie chart to show the distribution of accuracy scores across datasets.
C.Create a bar chart comparing accuracy for different hyperparameter settings.
D.Apply PCA (Principal Component Analysis) to reduce the dimensionality of hyperparameter settings.

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Question No : 15

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)

A.Present a simple line chart showing one aspect of the data.
B.Use a heatmap to represent correlations between variables.
C.Generate a detailed text report with all the raw data.
D.Visualize all data in a single pie chart.
E.Create interactive dashboards using tools like Tableau or Power B

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NVIDIA NVD-001 시험