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

質問 1

In which industry has AI most significantly improved operational efficiency through predictive maintenance, leading to reduced downtime and maintenance costs?

（A）Finance

（B）Retail

（C）Manufacturing

（D）Healthcare

正解：C 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 features of GPUs is most crucial for accelerating AI workloads, specifically in the context of deep learning?

（A）Large amount of onboard cache memory

（B）High clock speed

（C）Ability to execute parallel operations across thousands of cores

（D）Lower power consumption compared to CPUs

正解：C 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

In managing an AI data center, you need to ensure continuous optimal performance and quickly respond to any potential issues. Which monitoring tool or approach would best suit the need to monitor GPU health, usage, and performance metrics across all deployed AI workloads?

（A）Splunk

（B）Prometheus with Node Exporter

（C）NVIDIA DCGM (Data Center GPU Manager)

（D）Nagios Monitoring System

正解：C 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 working on a project that involves both real-time AI inference and data preprocessing tasks. The AI models require high throughput and low latency, while the data preprocessing involves complex logic and diverse data types. Given the need to balance these tasks, which computing architecture should you prioritize for each task?

（A）Deploy AI inference on CPUs and data preprocessing on FPGAs

（B）Use CPUs for both AI inference and data preprocessing

（C）Prioritize GPUs for AI inference and CPUs for data preprocessing

（D）Use GPUs for both AI inference and data preprocessing

正解：C 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

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 youuse to allocate GPU resources across the cluster?

（A）Use a priority queue to assign GPUs to jobs based on their deadline, ensuring the most time-sensitive jobs complete first.

（B）Assign jobs to GPUs based on their geographic proximity to reduce data transfer times.

（C）Allocate GPUs to jobs based on their compute intensity, reserving the most powerful GPUs for the most demanding tasks.

（D）Allocate all GPUs to the largest job to ensure its rapid completion, then proceed with smaller jobs.

正解：C 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

A research team is deploying a deep learning model on an NVIDIA DGX A100 system. The model has high computational demands and requires efficient use of all available GPUs. During the deployment, they notice that the GPUs are underutilized, and the inter-GPU communication seems to be a bottleneck. The software stack includes TensorFlow, CUDA, NCCL, and cuDNN. Which of the following actions would most likely optimize the inter-GPU communication and improve overall GPU utilization?

（A）Increase the number of data parallel jobs running simultaneously.

（B）Switch to using a single GPU to reduce complexity.

（C）Ensure NCCL is configured correctly for optimal bandwidth utilization.

（D）Disable cuDNN to streamline GPU operations.

正解：C 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

Your team is tasked with accelerating a large-scale deep learning training job that involves processing a vast amount of data with complex matrix operations. The current setup uses high-performance CPUs, but the training time is still significant. Which architectural feature of GPUs makes them more suitable than CPUs for this task?

（A）Low power consumption

（B）High core clock speed

（C）Massive parallelism with thousands of cores

（D）Large cache memory

正解：C 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

You are tasked with contributing to the operations of an AI data center that requires high availability and minimal downtime. Which strategy would most effectively help maintain continuous AI operations in collaboration with the data center administrator?

（A）Implement a failover system where DPUs manage the AI model inference during GPU downtime

（B）Schedule regular maintenance during peak hours to ensure that GPUs and DPUs are always operational

（C）Use GPUs in active-passive clusters, with DPUs handling real-time network failover and security

（D）Deploy a redundant set of CPUs to take over GPU workloads in case of failure

正解：C 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 managing an AI cluster with several nodes, each equipped with multiple NVIDIA GPUs. The cluster supports various machine learning tasks with differing resource requirements. Some jobs are GPU-intensive, while others require high memory but minimal GPU usage. Your goal is to efficiently allocate resources to maximize throughput and minimize job wait times. Which orchestration strategy would best optimize resource allocation in this mixed-workload environment?

（A）Allocate GPUs evenly across all jobs to ensure fair distribution.

（B）Manually assign jobs to specific nodes based on estimated workload requirements.

（C）Use a dynamic scheduler that adjusts resource allocation based on job requirements and current cluster utilization.

（D）Schedule jobs based on a fixed priority order, regardless of resource requirements.

正解：C 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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