NCA-AIIO無料問題集「NVIDIA-Certified Associate AI Infrastructure and Operations」

質問 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）Memory Bandwidth Utilization

（B）GPU Temperature and Thermal Status

（C）GPU Clock Speed

（D）CPU Utilization

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

Your AI cluster is managed using Kubernetes with NVIDIA GPUs. Due to a sudden influx of jobs, your cluster experiences resource overcommitment, where more jobs are scheduled than the available GPU resources can handle. Which strategy would most effectively manage this situation to maintain cluster stability?

（A）Implement Resource Quotas and LimitRanges in Kubernetes

（B）Increase the Maximum Number of Pods per Node

（C）Use Kubernetes Horizontal Pod Autoscaler Based on Memory Usage

（D）Schedule Jobs in a Round-Robin Fashion Across Nodes

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

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）Network Latency

（D）CPU Utilization

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

Your organization is setting up an AI infrastructure to support a range of AI workloads, including data processing, model training, and inference. The infrastructure needs to be scalable, support distributed training, and handle large datasets efficiently. Which NVIDIA solution would be most suitable for managing and orchestrating this AI infrastructure?

（A）NVIDIA DGX Systems

（B）NVIDIA DeepOps

（C）NVIDIA TensorRT

（D）NVIDIA RAPIDS

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

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）Different GPU models are used in the same setup.

（B）GPUs are not properly installed in the server chassis.

（C）The AI model code is optimized only for specific GPUs.

（D）Data loading process is not evenly distributed across GPUs.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

You are managing an AI infrastructure using NVIDIA GPUs to train large language models for a social media company. During training, you observe that the GPU utilization is significantly lower than expected, leading to longer training times. Which of the following actions is most likely to improve GPU utilization and reduce training time?

（A）Use mixed precision training

（B）Reduce the learning rate

（C）Increase the batch size during training

（D）Decrease the model complexity

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

A retail company wants to implement an AI-based system to predict customer behavior and personalize product recommendations across its online platform. The system needs to analyze vast amounts of customer data, including browsing history, purchase patterns, and social media interactions. Which approach would be the most effective for achieving these goals?

（A）Using a simple linear regression model to predict customer behavior based on purchase history alone

（B）Utilizing unsupervised learning to automatically classify customers into different categories without labeled data

（C）Implementing a rule-based AI system to generate recommendations based on predefined customer criteria

（D）Deploying a deep learning model that uses a neural network with multiple layers for feature extraction and prediction

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

You are tasked with designing a highly available AI data center platform that can continue to operate smoothly even in the event of hardware failures. The platform must support both training and inference workloads with minimal downtime. Which architecture would best meet these requirements?

（A）Set up a warm standby system where another data center mirrors the primary one and is manually activated

（B）Deploy a single, powerful GPU server with redundant power supplies and network interfaces

（C）Use a cluster of CPU-based servers with RAID storage to ensure data redundancy and protection

（D）Implement a distributed architecture with multiple GPU servers and a load balancer to distribute the workload

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 9

Which of the following best describes the primary benefit of using GPUs over CPUs for AI workloads?

（A）GPUs are designed to handle parallel processing tasks efficiently.

（B）GPUs consume less power than CPUs for AI tasks.

（C）GPUs provide better accuracy in AI model predictions.

（D）GPUs have higher memory capacity than CPUs.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

Your team is running an AI inference workload on a Kubernetes cluster with multiple NVIDIA GPUs. You observe that some nodes with GPUs are underutilized, while others are overloaded, leading to inconsistent inference performance across the cluster. Which strategy would most effectively balance the GPU workload across the Kubernetes cluster?

（A）Reducing the number of GPU nodes in the cluster

（B）Deploying a GPU-aware scheduler in Kubernetes

（C）Using CPU-based autoscaling to balance the workload

（D）Implementing GPU resource quotas to limit GPU usage per pod

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

NCA-AIIO 無料問題集「NVIDIA-Certified Associate AI Infrastructure and Operations」

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