NCA-AIIO試験無料問題集（199題）「NVIDIA-Certified Associate AI Infrastructure and Operations 認定」

出題：1

Which NVIDIA software component is specifically designed to accelerate the end-to-end data science workflow by leveraging GPU acceleration?

A. NVIDIA RAPIDS

B. NVIDIA CUDA Toolkit

C. NVIDIA TensorRT

D. NVIDIA DeepStream SDK

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

You are assisting in a project where the senior engineer requires you to create visualizations of system resource usage during the training of an AI model. The training was conducted using multiple NVIDIA GPUs over several hours. The goal is to present the results in a way that highlights periods of high resource utilization and potential bottlenecks. Which type of visualization would best illustrate periods of high resource utilization and potential bottlenecks during the training process?

A. Box plot showing the distribution of resource usage.

B. Heatmap showing GPU utilization over time.

C. Pie chart showing the proportion of time each GPU was utilized.

D. Stacked bar chart showing cumulative resource usage.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

Your organization is running a mixed workload environment that includes both general-purpose computing tasks (like database management) and specialized tasks (like AI model inference). You need to decide between investing in more CPUs or GPUs to optimize performance and cost-efficiency. How does the architecture of GPUs compare to that of CPUs in this scenario?

A. GPUs are better suited for workloads requiring massive parallelism, while CPUs handle single-threaded tasks more efficiently

B. CPUs have more cores than GPUs, making them better for all types of workloads

C. GPUs are optimized for general-purpose computing and can replace CPUs entirely

D. CPUs and GPUs have identical architectures but differ only in power consumption

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

Your AI team is deploying a large-scale inference service that must process real-time data 24/7. Given the high availability requirements and the need to minimize energy consumption, which approach would best balance these objectives?

A. Schedule inference tasks to run in batches during off-peak hours

B. Use a GPU cluster with a fixed number of GPUs always running at 50% capacity to save energy

C. Implement an auto-scaling group of GPUs that adjusts the number of active GPUs based on the workload

D. Use a single powerful GPU that operates continuously at full capacity to handle all inference tasks

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

A tech startup is building a high-performance AI application that requires processing large datasets and performing complex matrix operations. The team is debating whether to use GPUs or CPUs to achieve the best performance. What is the most compelling reason to choose GPUs over CPUs for this specific use case?

A. GPUs excel at parallel processing, which is ideal for handling large datasets and performing complex matrix operations

B. GPUs have higher single-thread performance, which is crucial for AI tasks

C. GPUs have larger memory caches than CPUs, which speeds up data retrieval for AI processing

D. GPUs consume less power than CPUs, making them more energy-efficient for AI tasks

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：6

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. Large cache memory

B. Low power consumption

C. Massive parallelism with thousands of cores

D. High core clock speed

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

When implementing an MLOps pipeline, which component is crucial for managing version control and tracking changes in model experiments?

A. Orchestration Platform

B. Model Registry

C. Artifact Repository

D. Continuous Integration (CI) System

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

You are helping a senior engineer analyze the results of a hyperparameter tuning process for a machine learning model. The results include a large number of trials, each with different hyperparameters and corresponding performance metrics. The engineer asks you to create visualizations that will help in understanding how different hyperparameters impact model performance. Which type of visualization would be most appropriate for identifying the relationship between hyperparameters and model performance?

A. Parallel coordinates plot showing hyperparameters and performance metrics

B. Pie chart showing the proportion of successful trials

C. Line chart showing performance metrics over trials

D. Scatter plot of hyperparameter values against performance metrics

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：9

A transportation company wants to implement AI to improve the safety and efficiency of its autonomous vehicle fleet. They need a solution that can handle real-time data processing, deep learning model inference, and high-throughput workloads. Which NVIDIA solution should they consider deploying?

A. NVIDIA DeepStream

B. NVIDIA Jetson

C. NVIDIA Clara

D. NVIDIA Drive

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：10

An AI operations team is tasked with monitoring a large-scale AI infrastructure where multiple GPUs are utilized in parallel. To ensure optimal performance and early detection of issues, which two criteria are essential for monitoring the GPUs? (Select two)

A. Average CPU temperature

B. Number of active CPU threads

C. Memory bandwidth usage on GPUs

D. GPU fan noise levels

E. GPU utilization percentage

正解：C,E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

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