最新なNVIDIA NCA-AIIO問題集（199題）、真実試験の問題を全部にカバー！

質問 1：
Your AI infrastructure team is observing out-of-memory (OOM) errors during the execution of large deep learning models on NVIDIA GPUs. To prevent these errors and optimize model performance, which GPU monitoring metric is most critical?
A. GPU Memory Usage
B. GPU Core Utilization
C. PCIe Bandwidth Utilization
D. Power Usage
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 2：
Which statement correctly differentiates between AI, machine learning, and deep learning?
A. Deep learning is a broader concept than machine learning, which is a specialized form of AI.
B. Machine learning is a type of AI that only uses linear models, while deep learning involves non-linear models exclusively.
C. Machine learning is the same as AI, and deep learning is simply a method within AI that doesn't involve machine learning.
D. AI is a broad field encompassing various technologies, including machine learning, which focuses on data-driven models, and deep learning, a subset of machine learning using neural networks.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 3：
In a distributed AI training environment, you notice that the GPU utilization drops significantly when the model reaches the backpropagation stage, leading to increased training time. What is the most effective way to address this issue?
A. Optimize the data loading pipeline to ensure continuous GPU data feeding during backpropagation
B. Increase the learning rate to speed up the training process
C. Implement mixed-precision training to reduce the computational load during backpropagation
D. Increase the number of layers in the model to create more work for the GPUs during backpropagation
正解：C
解説: (Pass4Test メンバーにのみ表示されます)

質問 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. Dual-axis line chart with accuracy on one axis and latency on the other.
B. Stacked area chart showing cumulative accuracy and latency.
C. Box plot comparing accuracy and latency.
D. Pie chart showing the distribution of accuracy metrics.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 5：
You are assisting in a project that involves deploying a large-scale AI model on a multi-GPU server. The server is experiencing unexpected performance degradation during inference, and you have been asked to analyze the system under the supervision of a senior engineer. Which approach would be most effective in identifying the source of the performance degradation?
A. Check the system's CPU utilization.
B. Monitor the system's power supply levels.
C. Analyze the GPU memory usage using nvidia-smi.
D. Inspect the training data for inconsistencies.
正解：C
解説: (Pass4Test メンバーにのみ表示されます)

質問 6：
After deploying an AI model on an NVIDIA T4 GPU in a production environment, you notice that the inference latency is inconsistent, varying significantly during different times of the day. Which of the following actions would most likely resolve the issue?
A. Upgrade the GPU driver.
B. Increase the number of inference threads.
C. Deploy the model on a CPU instead of a GPU.
D. Implement GPU isolation for the inference process.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 7：
You are comparing several regression models that predict the future sales of a product based on historical data. The models vary in complexity and computational requirements. Your goal is to select the model that provides the best balance between accuracy and the ability to generalize to new data. Which performance metric should you prioritize to select the most reliable regression model?
A. Mean Squared Error (MSE)
B. Cross-Entropy Loss
C. Accuracy
D. R-squared (Coefficient of Determination)
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 8：
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?
A. Use CPU-based Inference for Less Critical Workloads
B. Upgrade the GPUs in the Cluster to More Powerful Models
C. Implement Model Optimization Techniques
D. Implement GPU Quotas with Kubernetes Resource Management
正解：D
解説: (Pass4Test メンバーにのみ表示されます)