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[General] Latest NCA-AIIO Exam Practice, NCA-AIIO Test Dumps Free

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【General】 Latest NCA-AIIO Exam Practice, NCA-AIIO Test Dumps Free

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q18-Q23):NEW QUESTION # 18
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. cuDNN
  • D. NCCL
Answer: C
Explanation:
NVIDIA cuDNN (CUDA Deep Neural Network library) is specifically designed to optimize deep learning operations on NVIDIA GPUs by providing highly tuned implementations of standard routines, such as convolutions, pooling, and activation functions. It underpins frameworks like TensorFlow and PyTorch, accelerating training and inference in NVIDIA's ecosystem (e.g., DGX, Jetson). cuDNN's optimizations leverage GPU parallelism, making it the core component for deep learning performance.
CUDA (Option A) is a general-purpose GPU programming platform, not specialized for deep learning.
TensorFlow (Option B) is a framework that uses cuDNN, not the optimizer itself. NCCL (Option D) focuses on multi-GPU communication, not individual operations. cuDNN is NVIDIA's flagship deep learning optimization tool.

NEW QUESTION # 19
Your AI data center is running multiple high-performance GPU workloads, and you notice that certain servers are being underutilized while others are consistently at full capacity, leading to inefficiencies. Which of the following strategies would be most effective in balancing the workload across your AI data center?
  • A. Increase cooling capacity in the data center
  • B. Use horizontal scaling to add more servers
  • C. Manually reassign workloads based on current utilization
  • D. Implement NVIDIA GPU Operator with Kubernetes for automatic resource scheduling
Answer: D
Explanation:
The NVIDIA GPU Operator with Kubernetes (C) automates resource scheduling and workload balancing across GPU clusters. It integrates GPU awareness into Kubernetes, dynamically allocating workloads to underutilized servers based on real-time utilization, priority, and resource demands. This ensures efficient use of all GPUs, reducing inefficiencies without manual intervention.
* Horizontal scaling(A) adds more servers, increasing capacity but not addressing the imbalance- underutilized servers would remain inefficient.
* Manual reassignment(B) is impractical for large-scale, dynamic workloads and lacks scalability.
* Increasing cooling capacity(D) improves hardware reliability but doesn't balanceworkloads.
The GPU Operator's automation and integration with Kubernetes make it the most effective solution (C).

NEW QUESTION # 20
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. Remote Direct Memory Access (RDMA)
  • C. HTTP
  • D. TCP/IP
Answer: B
Explanation:
Remote Direct Memory Access (RDMA) is the most effective protocol for optimizing network performance between storage systems and GPU compute nodes in an AI infrastructure. RDMA enables direct memory access between devices over high-speed interconnects (e.g., InfiniBand, RoCE), bypassing the CPU and reducing latency while providing high bandwidth. This is critical for AI workloads, where large datasets must move quickly to GPUs for training or inference, minimizing bottlenecks.
HTTP (A) and SMTP (B) are application-layer protocols for web and email, respectively, unsuitable for low- latency data movement. TCP/IP (D) is a general-purpose networking protocol but lacks the performance of RDMA for GPU-centric workloads. NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure and Operations" materials highlight RDMA's role in high-performance AI networking.

NEW QUESTION # 21
In a virtualized AI environment, you are responsible for managing GPU resources across several VMs running different AI workloads. Which approach would most effectively allocate GPU resources to maximize performance and flexibility?
  • A. Deploy all AI workloads in a single VM with multiple GPUs to centralize resource management
  • B. Use GPU passthrough to allocate full GPU resources directly to one VM at a time, based on the highest priority workload
  • C. Assign a dedicated GPU to each VM to ensure consistent performance for each AI workload
  • D. Implement GPU virtualization to allow multiple VMs to share GPU resources dynamically based on demand
Answer: D
Explanation:
Implementing GPU virtualization to allow multiple VMs to share GPU resources dynamically based on demand is the most effective approach for maximizing performance and flexibility in a virtualized AI environment. NVIDIA's GPU virtualization (e.g., via vGPU or GPU Operator in Kubernetes) enables time- slicing or partitioning (e.g., MIG on A100 GPUs), allowing workloads to access GPU resources as needed.
This optimizes utilization and adapts to varying demands, as outlined in NVIDIA's "GPU Virtualization Guide" and "AI Infrastructure for Enterprise." A single VM (A) limits scalability. Dedicated GPUs per VM (B) wastes resources when idle. GPU passthrough (D) restricts sharing, reducing flexibility. NVIDIA recommends virtualization for efficient resource allocation in virtualized AI setups.

NEW QUESTION # 22
You are configuring a multi-node AI training environment using NVIDIA GPUs, and your team wants to ensure that the network infrastructure can handle the data transfer between nodes efficiently, especially during distributed training tasks. What is the most critical factor to consider in the network infrastructure to minimize bottlenecks during distributed AI training?
  • A. Increasing the number of Ethernet ports on each node
  • B. Reducing the number of nodes to simplify the network
  • C. Implementing InfiniBand with RDMA support
  • D. Using software-defined networking (SDN) to manage traffic
Answer: C
Explanation:
Implementing InfiniBand with RDMA support is the most critical factor to minimize bottlenecks in distributed AI training. It provides ultra-low latency and high bandwidth (e.g., 200 Gb/s), optimizing GPU-to- GPU data transfers via NCCL. Option B (more Ethernet ports) improves redundancy, not speed. Option C (fewer nodes) limits scalability. Option D (SDN) aids management, not raw performance. NVIDIA's DGX networking guides recommend InfiniBand.

NEW QUESTION # 23
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