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

質問 1

Which of the following is a primary challenge when integrating AI into existing IT infrastructure?

（A）Finding AI tools that are compatible with existing hardware

（B）Ensuring AI models have a user-friendly interface

（C）Selecting the right cloud service provider

（D）Scalability of the AI workloads

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 2

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 3

A financial institution is implementing a real-time fraud detection system using deep learning models. The system needs to process large volumes of transactions with very low latency to identify fraudulent activities immediately. During testing, the team observes that the system occasionally misses fraudulent transactions under heavy load, and latency spikes occur. Which strategy would best improve the system's performance and reliability?

（A）Reduce the complexity of the model to decrease the inference time.

（B）Implement model parallelism to split the model across multiple GPUs.

（C）Deploy the model on a CPU cluster instead of GPUs to handle the processing.

（D）Increase the dataset size by including more historical transaction data.

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

You are managing a data center running numerous AI workloads on NVIDIA GPUs. Recently, some of the GPUs have been showing signs of underperformance, leading to slower job completion times. You suspect that resource utilization is not optimal. You need to implement monitoring strategies to ensure GPUs are effectively utilized and to diagnose any underperformance. Which of the following metrics is most critical to monitor for identifying underutilized GPUs in your data center?

（A）GPU Core Utilization

（B）Network Bandwidth Utilization

（C）System Uptime

（D）GPU Memory Usage

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 5

In an AI-focused data center, ensuring high data throughput is critical for feeding large datasets to training models efficiently. Which strategy would best optimize data throughput in this environment?

（A）Implement NVMe SSDs for faster data access and higher throughput.

（B）Use traditional HDD storage systems due to their high storage capacity.

（C）Use a RAID 5 configuration to increase redundancy and throughput.

（D）Implement a distributed file system without considering the underlying hardware.

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 6

Which NVIDIA hardware and software combination is best suited for training large-scale deep learning models in a data center environment?

（A）NVIDIA Quadro GPUs with RAPIDS for real-time analytics

（B）NVIDIA Jetson Nano with TensorRT for training

（C）NVIDIA DGX Station with CUDA toolkit for model deployment

（D）NVIDIA A100 Tensor Core GPUs with PyTorch and CUDA for model training

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 7

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 8

A large enterprise is deploying a high-performance AI infrastructure to accelerate its machine learning workflows. They are using multiple NVIDIA GPUs in a distributed environment. To optimize the workload distribution and maximize GPU utilization, which of the following tools or frameworks should be integrated into their system? (Select two)

（A）NVIDIA NGC (NVIDIA GPU Cloud)

（B）NVIDIA CUDA

（C）Keras

（D）TensorFlow Serving

（E）NVIDIA NCCL (NVIDIA Collective Communications Library)

正解：B、E 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

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 RAPIDS + NVIDIA Triton Inference Server + NVIDIA NGC Catalog

（B）NVIDIA RAPIDS + NVIDIA TensorRT

（C）NVIDIA Triton Inference Server + NVIDIA NGC Catalog

（D）NVIDIA TensorRT + NVIDIA DeepStream SDK

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

You are working with a large dataset containing millions of records related to customer behavior. Your goal is to identify key trends and patterns that could improve your company's product recommendations. You have access to a high-performance AI infrastructure with NVIDIA GPUs, and you want to leverage this for efficient data mining. Which technique would most effectively utilize the GPUs to extract actionable insights from the dataset?

（A）Using traditional SQL queries to filter and sort the data

（B）Visualizing the data using a standard spreadsheet application

（C）Employing a simple decision tree model to classify customer data

（D）Implementing deep learning models for clustering customers into segments

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)

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

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