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[General] NCA-AIIO Real Brain Dumps, Practice NCA-AIIO Exams Free

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【General】 NCA-AIIO Real Brain Dumps, Practice NCA-AIIO Exams Free

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q25-Q30):NEW QUESTION # 25
Which statement correctly differentiates between AI, machine learning, and deep learning?
  • A. AI is a broad field encompassing various technologies, including machine learning, which focuses on data-driven models, and deep learning, a subset of machine learning using neural networks.
  • B. Machine learning is the same as AI, and deep learning is simply a method within AI that doesn't involve machine learning.
  • C. Deep learning is a broader concept than machine learning, which is a specialized form of AI.
  • D. Machine learning is a type of AI that only uses linear models, while deep learning involves non-linear models exclusively.
Answer: A
Explanation:
AI is a broad field encompassing technologies for intelligent systems. Machine learning (ML), a subset, uses data-driven models, while deep learning (DL), a subset of ML, employs neural networks for complex tasks.
NVIDIA's ecosystem (e.g., cuDNN for DL, RAPIDS for ML) reflects this hierarchy, supporting all levels.
Option A misaligns ML and DL. Option C reverses the subset order. Option D oversimplifies ML and DL distinctions. Option B matches NVIDIA's conceptual framework.

NEW QUESTION # 26
You are working on a project that involves both real-time AI inference and data preprocessing tasks. The AI models require high throughput and low latency, while the data preprocessing involves complex logic and diverse data types. Given the need to balance these tasks, which computing architecture should you prioritize for each task?
  • A. Use CPUs for both AI inference and data preprocessing
  • B. Deploy AI inference on CPUs and data preprocessing on FPGAs
  • C. Prioritize GPUs for AI inference and CPUs for data preprocessing
  • D. Use GPUs for both AI inference and data preprocessing
Answer: C
Explanation:
Prioritizing GPUs for AI inference and CPUs for data preprocessing is the best architecture to balance these tasks. GPUs excel at parallel computation, making them ideal for high-throughput, low-latency inference using NVIDIA tools like TensorRT or Triton. CPUs, with fewer but more powerful cores, handle complex, sequential preprocessing tasks (e.g., data cleaning, branching logic) efficiently, as noted in NVIDIA's "AI Infrastructure for Enterprise" and "GPU Architecture Overview." This hybrid approach leverages each processor's strengths, optimizing overall performance.
Using GPUs for both (A) underutilizes CPUs for preprocessing. CPUs for both (B) sacrifices inference performance. CPUs for inference and FPGAs for preprocessing (D) misaligns with NVIDIA GPU strengths and adds complexity. NVIDIA recommends this CPU-GPU division.

NEW QUESTION # 27
You are responsible for managing an AI infrastructure that includes multiple GPU clusters for deep learning workloads. One of your tasks is to efficiently allocate resources and manage workloads across these clusters using an orchestration platform. Which of the following approaches would best optimize the utilization of GPU resources while ensuring high availability of the AI workloads?
  • A. Use a round-robin scheduling algorithm across all GPU clusters
  • B. Implement a load-balancing algorithm that dynamically assigns workloads based on real-time GPU availability
  • C. Use a first-come, first-served (FCFS) scheduling policy across all clusters
  • D. Assign workloads to clusters based on a predefined static schedule
Answer: B
Explanation:
Implementing a load-balancing algorithm that dynamically assigns workloads based on real-time GPU availability is the best approach to optimize resource utilization and ensure high availability in multi-cluster GPU environments. This method, supported by NVIDIA's "DeepOps" and Kubernetes with GPU Operator, monitors GPU metrics (e.g., utilization, memory) via tools like DCGM and allocates workloads to underutilized clusters, preventing bottlenecks and ensuring failover. This dynamic approach adapts to workload changes, maximizing efficiency and uptime.
Round-robin (A) and FCFS (D) ignore real-time resource states, leading to inefficiency. Static scheduling (B) lacks adaptability. NVIDIA's orchestration guidelines favor dynamic load balancing for AI clusters.

NEW QUESTION # 28
What is an advantage of InfiniBand over Ethernet?
  • A. InfiniBand supports RDMA while Ethernet does not.
  • B. InfiniBand offers lower latency than Ethernet.
  • C. InfiniBand always provides higher bandwidth than Ethernet.
Answer: B
Explanation:
InfiniBand's advantage over Ethernet lies in its lower latency, achieved through a streamlined protocol and hardware offloads, delivering microsecond-scale communication critical for AI clusters. While InfiniBand often offers high bandwidth, Ethernet can match or exceed it (e.g., 400 GbE), and Ethernet supports RDMA via RoCE, making latency the standout differentiator.
(Reference: NVIDIA Networking Documentation, Section on InfiniBand vs. Ethernet)

NEW QUESTION # 29
When designing a data center specifically for AI workloads, which of the following factors is most critical to optimize for training large-scale neural networks?
  • A. Deploying the maximum number of CPU cores available in each node
  • B. Ensuring the data center has a robust virtualization platform
  • C. High-speed, low-latency networking between compute nodes
  • D. Maximizing the number of storage arrays to handle data volumes
Answer: C
Explanation:
High-speed, low-latency networking between compute nodes is the most critical factor to optimize when designing a data center for training large-scale neural networks. AI workloads, especially distributed training on NVIDIA GPUs (e.g., DGX systems), require rapid communication between nodes to exchange gradients, weights, and other data. Technologies like NVIDIA NVLink (intra-node) and InfiniBand or RDMA (inter- node) minimize communication overhead, ensuringscalability and reduced training time. NVIDIA's "DGX SuperPOD Reference Architecture" highlights that networking performance is a bottleneck in large-scale AI training, making it more critical than storage or CPU capacity.
Maximizing storage arrays (A) is important for data availability but less critical than networking for training performance. CPU cores (B) play a secondary role to GPUs in AI training. Virtualization (D) enhances flexibility but is not the primary optimization focus for training throughput. NVIDIA's AI infrastructure guidelines prioritize networking for such workloads.

NEW QUESTION # 30
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