NCA-AIIOの無料問題集（199題）、NCA-AIIO認定試験参考書

NVIDIA NCA-AIIO 問題集

試験コード：NCA-AIIO

試験名称：NVIDIA-Certified Associate AI Infrastructure and Operations

最近更新時間：2025-04-15

問題と解答：全199問

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質問 1：
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. Implement model parallelism to split the model across multiple GPUs.
B. Reduce the complexity of the model to decrease the inference time.
C. Increase the dataset size by including more historical transaction data.
D. Deploy the model on a CPU cluster instead of GPUs to handle the processing.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
Which of the following has been the most critical factor enabling the recent rapid improvements and adoption of AI in various sectors?
A. The availability of large, annotated datasets for training AI models.
B. Increased investment in AI research and development by large tech companies.
C. The rise of user-friendly AI frameworks and libraries.
D. The development and adoption of AI-specific hardware like GPUs and TPUs.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 3：
Your organization is building a hybrid cloud system that needs to handle a variety of tasks, including complex scientificsimul-ations, database management, and training large AI models. You need to allocate resources effectively. How do GPU and CPU architectures compare in terms of handling these different tasks?
A. GPUs are better for parallel tasks like AI model training andsimul-ations, while CPUs are better for sequential tasks like database management.
B. GPUs are superior for all types of workloads in this scenario.
C. CPUs should be used for training AI models, while GPUs are better for database management.
D. GPUs should be used exclusively for scientificsimul-ations, and CPUs for everything else.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A data center is running a cluster of NVIDIA GPUs to support various AI workloads. The operations team needs to monitor GPU performance to ensure workloads are running efficiently and to prevent potential hardware failures. Which two key measures should they focus on to monitor the GPUs effectively? (Select two)
A. Network bandwidth usage
B. GPU memory utilization
C. Disk I/O rates
D. GPU temperature and power consumption
E. CPU clock speed
正解：B,D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You are tasked with optimizing the performance of a deep learning model used for image recognition. The model needs to process a large dataset as quickly as possible while maintaining high accuracy. You have access to both GPU and CPU resources. Which two statements best describe why GPUs are more suitable than CPUs for this task? (Select two)
A. GPUs have a lower latency than CPUs, making them faster for individual calculations.
B. GPUs have a higher number of cores compared to CPUs, allowing for parallel processing of many operations simultaneously.
C. CPUs are better suited for handling the large dataset due to their superior memory bandwidth.
D. CPUs consume less power than GPUs, making them more suitable for prolonged computations.
E. GPUs are optimized for matrix operations, which are common in deep learning algorithms.
正解：B,E
解説: (Topexam メンバーにのみ表示されます)

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NVIDIA-Certified Associate AI Infrastructure and Operations 認定 NCA-AIIO 試験問題:

1. 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) Increase the Maximum Number of Pods per Node
B) Use Kubernetes Horizontal Pod Autoscaler Based on Memory Usage
C) Schedule Jobs in a Round-Robin Fashion Across Nodes
D) Implement Resource Quotas and LimitRanges in Kubernetes

2. 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) Use a GPU cluster with a fixed number of GPUs always running at 50% capacity to save energy
B) Schedule inference tasks to run in batches during off-peak hours
C) Use a single powerful GPU that operates continuously at full capacity to handle all inference tasks
D) Implement an auto-scaling group of GPUs that adjusts the number of active GPUs based on the workload

3. Which of the following software components is most responsible for optimizing deep learning operations on NVIDIA GPUs by providing highly tuned implementations of standard routines?

A) CUDA
B) TensorFlow
C) NCCL
D) cuDNN

4. You are responsible for managing an AI-driven fraud detection system that processes transactions in real- time. The system is hosted on a hybrid cloud infrastructure, utilizing both on-premises and cloud-based GPU clusters. Recently, the system has been missing fraud detection alerts due to delays in processing data from on- premises servers to the cloud, causing significant financial risk to the organization. What is the most effective way to reduce latency and ensure timely fraud detection across the hybrid cloud environment?

A) Switching to a single-cloud provider to centralize all processing in the cloud
B) Migrating the entire fraud detection workload to on-premises servers
C) Increasing the number of on-premises GPU clusters to handle the workload locally
D) Implementing a low-latency, high-throughput direct connection between the on-premises data center and the cloud

5. 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) Deploying a deep learning model that uses a neural network with multiple layers for feature extraction and prediction
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) Using a simple linear regression model to predict customer behavior based on purchase history alone

質問と回答：

質問 # 1
正解： D

質問 # 2
正解： D

質問 # 3
正解： D

質問 # 4
正解： D

質問 # 5
正解： A