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最新的 NVIDIA-Certified Associate NCA-AIIO 免費考試真題:

1. In an AI infrastructure setup, you need to optimize the network for high-performance data movement between storage systems and GPU compute nodes. Which protocol would be most effective for achieving low latency and high bandwidth in this environment?

A) SMTP
B) TCP/IP
C) Remote Direct Memory Access (RDMA)
D) HTTP

2. Your company is implementing a hybrid cloud AI infrastructure that needs to support both on-premises and cloud-based AI workloads. The infrastructure must enable seamless integration, scalability, and efficient resource management across different environments. Which NVIDIA solution should be considered to best support this hybrid infrastructure?

A) NVIDIA Clara Deploy SDK
B) NVIDIA Triton Inference Server
C) NVIDIA Fleet Command
D) NVIDIA MIG (Multi-Instance GPU)

3. A company is deploying a large-scale AI training workload that requires distributed computing across multiple GPUs. They need to ensure efficient communication between GPUs on different nodes and optimize the training time. Which of the following NVIDIA technologies should they use to achieve this?

A) NVIDIA NVLink
B) NVIDIA TensorRT
C) NVIDIA DeepStream SDK
D) NVIDIA NCCL (NVIDIA Collective Communication Library)

4. You are tasked with optimizing an AI-driven financial modeling application that performs both complex mathematical calculations and real-time data analytics. The calculations are CPU-intensive, requiring precise sequential processing, while the data analytics involves processing large datasets in parallel. How should you allocate the workloads across GPU and CPU architectures?

A) Use CPUs for data analytics and GPUs for mathematical calculations
B) Use CPUs for mathematical calculations and GPUs for data analytics
C) Use GPUs for mathematical calculations and CPUs for managing I/O operations
D) Use GPUs for both the mathematical calculations and data analytics

5. A large manufacturing company is implementing an AI-based predictive maintenance system to reduce downtime and increase the efficiency of its production lines. The AI system must analyze data from thousands of sensors in real-time to predict equipment failures before they occur. However, during initial testing, the system fails to process the incoming data quickly enough, leading to delayed predictions and occasional missed failures. What would be the most effective strategy to enhance the system's real-time processing capabilities?

A) Use a more complex AI model to enhance prediction accuracy
B) Increase the frequency of sensor data collection to provide more detailed inputs for the AI model
C) Implement edge computing to preprocess sensor data closer to the source before sending it to the central AI system
D) Reduce the number of sensors to decrease the amount of data the AI system must process

問題與答案：