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NCA-AIIO認證考試問題與答案

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NVIDIA NCA-AIIO 考試大綱：

主題
簡介

主題 1

Essential AI knowledge: Exam Weight: This section of the exam measures the skills of IT professionals and covers foundational AI concepts. It includes understanding the NVIDIA software stack, differentiating between AI, machine learning, and deep learning, and comparing training versus inference. Key topics also involve explaining the factors behind AI's rapid adoption, identifying major AI use cases across industries, and describing the purpose of various NVIDIA solutions. The section requires knowledge of the software components in the AI development lifecycle and an ability to contrast GPU and CPU architectures.

主題 2

AI Infrastructure: This section of the exam measures the skills of IT professionals and focuses on the physical and architectural components needed for AI. It involves understanding the process of extracting insights from large datasets through data mining and visualization. Candidates must be able to compare models using statistical metrics and identify data trends. The infrastructure knowledge extends to data center platforms, energy-efficient computing, networking for AI, and the role of technologies like NVIDIA DPUs in transforming data centers.

主題 3

AI Operations: This section of the exam measures the skills of data center operators and encompasses the management of AI environments. It requires describing essentials for AI data center management, monitoring, and cluster orchestration. Key topics include articulating measures for monitoring GPUs, understanding job scheduling, and identifying considerations for virtualizing accelerated infrastructure. The operational knowledge also covers tools for orchestration and the principles of MLOps.

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最新的 NVIDIA-Certified Associate NCA-AIIO 免費考試真題 (Q48-Q53):

問題 #48
What is the maximum number of MIG instances that an H100 GPU provides?

A. 0
B. 1
C. 2

答案：A

解題說明：
The NVIDIA H100 GPU supports up to 7 Multi-Instance GPU (MIG) partitions, allowing it to be divided into seven isolated instances for multi-tenant or mixed workloads. This capability leverages the H100's architecture to maximize resource flexibility and efficiency, with 7 being the documented maximum.
(Reference: NVIDIA H100 GPU Documentation, MIG Section)

問題 #49
An enterprise is deploying a large-scale AI model for real-time image recognition. They face challenges with scalability and need to ensure high availability while minimizing latency. Which combination of NVIDIA technologies would best address these needs?

A. NVIDIA TensorRT and NVLink
B. NVIDIA CUDA and NCCL
C. NVIDIA DeepStream and NGC Container Registry
D. NVIDIA Triton Inference Server and GPUDirect RDMA

答案：A

解題說明：
NVIDIA TensorRT and NVLink (D) best address scalability, high availability, and low latency forreal-time image recognition:
* NVIDIA TensorRToptimizes deep learning models for inference, reducing latency and increasing throughput on GPUs, critical for real-time tasks.
* NVLinkprovides high-speed GPU-to-GPU interconnects, enabling scalable multi-GPU setups with minimal data transfer latency, ensuring high availability and performance under load.
* CUDA and NCCL(A) are foundational for training, not optimized for inference deployment.
* DeepStream and NGC(B) focus on video analytics and container management, less suited for general image recognition scalability.
* Triton and GPUDirect RDMA(C) enhance inference and data transfer, but RDMA is more network- focused, less critical than NVLink for GPU scaling.
TensorRT and NVLink align with NVIDIA's inference optimization strategy (D).

問題 #50
When virtualizing a GPU-accelerated infrastructure to support AI operations, what is a key factor to ensure efficient and scalable performance across virtual machines (VMs)?

A. Ensure that GPU memory is not overcommitted among VMs.
B. Increase the CPU allocation to each VM.
C. Allocate more network bandwidth to the host machine.
D. Enable nested virtualization on the VMs.

答案：A

解題說明：
Ensuring that GPU memory is not overcommitted among VMs is a key factor for efficient and scalable performance in a virtualized GPU-accelerated infrastructure. NVIDIA's vGPU technology allows multiple VMs to share a GPU, but overcommitting memory (allocating more than physically available) causes contention, degrading performance. Proper memory allocation, as outlined in NVIDIA's vGPU documentation, ensures each VM has sufficient resources for AI workloads. Option A (more CPU) doesn't address GPU bottlenecks. Option C (network bandwidth) aids communication, not GPU efficiency. Option D (nested virtualization) adds complexity without direct benefit. NVIDIA emphasizes memory management for virtualization success.

問題 #51
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 NCCL (NVIDIA Collective Communication Library)
B. NVIDIA NVLink
C. NVIDIA DeepStream SDK
D. NVIDIA TensorRT

答案：A

解題說明：
NVIDIA NCCL (NVIDIA Collective Communication Library) is the optimal technology for ensuring efficient communication between GPUs across different nodes in a distributed AI training workload. NCCL is a library specifically designed for multi-GPU and multi-node communication, providing optimized collective operations (e.g., all-reduce, broadcast) that minimize latency and maximize bandwidth. It integrates with high- speed interconnects like NVLink (within a node) and InfiniBand (across nodes), making it ideal for large- scale training where GPUs must synchronize gradients and parameters efficiently to reduce training time.
NVIDIA NVLink (A) is a high-speed interconnect for GPU-to-GPU communication within a single node, but it does not address inter-node communication across a cluster. NVIDIA TensorRT (B) is an inference optimization library, not suited for training workloads. NVIDIA DeepStream SDK (D) focuses on real-time video processing and inference, not distributed training. Official NVIDIA documentation, such as the "NCCL Developer Guide" and "AI Infrastructure and Operations Fundamentals" course, confirms NCCL's role in optimizing distributed training performance.

問題 #52
You are managing an AI infrastructure that supports a healthcare application requiring high availability and low latency. The system handles multiple workloads, including real-time diagnostics, patient data analysis, and predictive modeling for treatment outcomes. To ensure optimal performance, which strategy should you adopt for workload distribution and resource management?

A. Allocate equal resources to all tasks to ensure uniform performance.
B. Implement an auto-scaling strategy that dynamically adjusts resources based on workload demands.
C. Manually allocate resources based on estimated task durations.
D. Prioritize real-time diagnostics by allocating the majority of resources to these tasks anddeprioritize others.

答案：B

解題說明：
In a healthcare application requiring high availability and low latency, such as one handling real-time diagnostics, patient data analysis, and predictive modeling, an auto-scaling strategy is critical. NVIDIA's AI infrastructure solutions, like those offered with NVIDIA DGX systems and NVIDIA AI Enterprise software, emphasize dynamic resource management to adapt to fluctuating workloads. Auto-scaling ensures that resources (e.g., GPU compute power, memory, and network bandwidth) are allocated based on real-time demand, which is essential for time-sensitive tasks like diagnostics that cannot tolerate delays. Option A (prioritizing diagnostics) might compromise other workloads like predictive modeling, leading to inefficiencies. Option B (manual allocation) is impractical for dynamic, unpredictable workloads, as it lacks adaptability and increases administrative overhead. Option D (equal allocation) fails to account for varying resource needs, potentially causing latency spikes in critical tasks. NVIDIA's documentation on AI Infrastructure for Enterprise highlights auto-scaling as a key feature for optimizing performance in hybrid and multi-workload environments, ensuring both high availability and low latency.

問題 #53
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